Dr. Uzzal Biswas | Khulna University (original) (raw)

Papers by Dr. Uzzal Biswas

Research paper thumbnail of KU-BdSL: An open dataset for Bengali sign language recognition

Data in Brief, 2023

Sign language is a form of communication medium for speech and hearing disabled people. It has v... more Sign language is a form of communication medium for
speech and hearing disabled people. It has various forms
with different troublesome patterns, which are difficult for
the general mass to comprehend. Bengali sign language
(BdSL) is one of the difficult sign languages due to its immense number of alphabet, words, and expression techniques. Machine translation can ease the difficulty for disabled people to communicate with generals. From the machine learning (ML) domain, computer vision can be the
solution for them, and every ML solution requires a optimized model and a proper dataset. Therefore, in this research
work, we have created a BdSL dataset and named ‘KU-BdSL’,
which consists of 30 classes describing 38 consonants (‘banjonborno’) of the Bengali alphabet. The dataset includes 1500
images of hand signs in total, each representing Bengali consonant(s). Thirty-nine participants (30 males and 9 females)
of different ages (21–38 years) participated in the creation
of this dataset. We adopted smartphones to capture the images due to the availability of their high-definition cameras.
We believe that this dataset can be beneficial to the deaf and
dumb (D&D) community. Identification of Bengali consonants
of BdSL from images or videos is feasible using the dataset.
It can also be employed for a human-machine interface for
disabled people. In the future, we will work on the vowels
and word level of BdSL.

Research paper thumbnail of Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care

Frontiers in Medicine, Feb 8, 2022

Discussion: Use of TCC was associated with several benefits, including higher patient engagement ... more Discussion: Use of TCC was associated with several benefits, including higher patient engagement and completion rates with CR. The conduct and delivery of TCC-Cardiac will be improved by the findings of this process evaluation to optimise recruitment, and establishing the roles of GPs and cardiologists as part of the model.

Research paper thumbnail of A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Diagnostics, 2023

Bibliometric analysis is a widely used technique for analyzing large quantities of academic liter... more Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

Research paper thumbnail of Demographic Factors That Influence Smartphone Ownership in a Cardiology Inpatient Population

Heart, Lung and Circulation, 2021

Research paper thumbnail of The Cost-effectiveness of TeleClinical Care: A Telemonitoring and Educational Smartphone App-based Model of Care

Heart, Lung and Circulation, 2021

Research paper thumbnail of Patterns and predictors of smartphone ownership in a cardiology inpatient population

European Heart Journal, 2021

Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chr... more Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chronic disease management. Uptake of these interventions depends on patient smartphone ownership. Purpose To examine the smartphone ownership rate among cardiac inpatients and identify the associated demographic factors. Methods Between February 2019 and March 2020, 565 patients were screened for potential enrolment in the TeleClinical Care (TCC) pilot study at two hospitals in Australia. All patients had an admission diagnosis of acute coronary syndrome or heart failure. Mobile phone ownership was documented at the time of screening. Retrospectively, each patient's electronic medical record was examined for: age, sex, primary diagnosis, suburb of residence, private health insurance subscription, smoking status and occupation. Continuous variables were analysed using a multinomial logistic regression model. Categorical variables were analysed using a generalised linear model. Results M...

Research paper thumbnail of Process Evaluation of the TeleClinical Care Randomised Controlled Trial

Heart, Lung and Circulation, 2021

Research paper thumbnail of Removing power line interference from ECG signal using adaptive filter and notch filter

2014 International Conference on Electrical Engineering and Information & Communication Technology, 2014

Performance of two adaptive filters, such as, normalized least-mean-square (NLMS) adaptive filter... more Performance of two adaptive filters, such as, normalized least-mean-square (NLMS) adaptive filter and recursive-least-square (RLS) adaptive filter are compared with a traditional notch filter both in time and frequency domains to remove the power line interference from the ECG signal. The power spectral density (PSD) and spectrogram analyses are also performed. Different performance parameters, such as, SNR, %PRD and MSE are also calculated. Real time recorded data from the benchmark MIT-BIH arrhythmia database has been used. The result demonstrates superior performance of adaptive NLMS filter for removing power line interference over adaptive RLS and notch filters.

Research paper thumbnail of An intelligent mHealth-based adjunct to improve the management of patients with cardiovascular disease

Regular recording of vital signs, modification of lifestyle behaviour and monitoring of health pr... more Regular recording of vital signs, modification of lifestyle behaviour and monitoring of health progress has been shown to be effective to better manage patients with cardiovascular disease (CVD). Despite this, there remain significant hospital readmissions due to CVD exacerbations. This thesis investigated if the readmission rate of CVD patients could be reduced through remote longitudinal monitoring of physiological measurements and by offering a mobile health (mHealth)-based adjunct to assist in lifestyle modification. The thesis also investigated if there was a relationship between patient engagement and their clinical outcomes.To improve the remote management of CVD patients, the architecture of an intelligent mHealth adjunct called Total Cardiac Care (TCC) was developed based around a smartphone app and wireless peripherals to record physiological data and patient activity. The system also enabled the clinician to regularly monitor the patients’ condition using a web portal, fa...

Research paper thumbnail of A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial

JMIR Mhealth Uhealth, 2022

Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are fr... more Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. Objective: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. Methods: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months.

Research paper thumbnail of Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care

Frontiers in Medicine, 2022

BackgroundA novel smartphone app-based model of care (TeleClinical Care – TCC) for patients with ... more BackgroundA novel smartphone app-based model of care (TeleClinical Care – TCC) for patients with acute coronary syndrome (ACS) and heart failure (HF) was evaluated in a two-site, pilot randomised control trial of 164 participants in Sydney, Australia. The program included a telemonitoring system whereby abnormal blood pressure, weight and heart rate readings were monitored by a central clinical team, who subsequently referred clinically significant alerts to the patients' usual general practitioner (GP, also known as primary care physician in the United States), HF nurse or cardiologist. While the primary endpoint, 30-day readmissions, was neutral, intervention arm participants demonstrated improvements in readmission rates over 6 months, cardiac rehabilitation (CR) completion and medication compliance. A process evaluation was designed to identify contextual factors and mechanisms that influenced the results, as well as strategies of improving site and participant recruitment a...

Research paper thumbnail of TeleClinical Care: A Randomised Control Trial of a Smartphone-Based Model of Care for Patients with Heart Failure or Acute Coronary Syndrome

Heart, Lung and Circulation, 2021

Research paper thumbnail of A randomised control trial of TeleClinical Care – a smartphone-app based model of care for heart failure and acute coronary syndromes

European Heart Journal, 2021

Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalis... more Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalisation and readmissions. A novel smartphone app-based model of care (TeleClinical Care – TCC) was developed to support patients after ACS or HF admission. Purpose This randomised control trial aimed to characterise both the intervention and clinical outcomes. The primary endpoint was the incidence of 30-day readmissions. Secondary endpoints included six-month cardiac and all-cause readmissions, mortality, major adverse cardiovascular events (MACE), cardiac rehabilitation (CR) completion, medication adherence, serum low-density lipoprotein (LDL-C), quality of life, blood pressure, body mass index, waist circumference and six-minute walk distance. Additionally, cost-effectiveness and user satisfaction were evaluated. Methods Patients were randomised 1:1 to either TCC plus usual care or usual care alone and were followed-up at six months. Intervention arm participants received the TCC app an...

Research paper thumbnail of 771 The Impact of Age on Smartphone Ownership in a Cardiology Inpatient Population

Heart, Lung and Circulation, 2020

Research paper thumbnail of Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic

Yearbook of Medical Informatics, 2021

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularit... more Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. Methods: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organizatio...

Research paper thumbnail of Telemedicine systems to manage chronic disease

Digital Health, 2021

The global burden of life-threatening chronic disease continues to increase and is mostly borne b... more The global burden of life-threatening chronic disease continues to increase and is mostly borne by older people. For the last two decades, the practice of telemedicine has focused on a patient-centric approach, which provides greater access to remote medical care at significantly lower cost. However, the manual nature of remote monitoring approaches creates new challenges regarding biosignal analysis and visualization. This chapter will provide an overview of existing issues related to intelligent remote patient monitoring and management. We will highlight the difficulties associated with the acquisition, analysis, interpretation and visualization of critical biosignals for meaningful diagnosis and intervention and suggest some possible pathways to address these problems. Finally, we will discuss potential future directions for telemedicine models based on intelligent decision support systems and data analytics.

Research paper thumbnail of TeleClinical Care: A randomised controlled trial of a smartphone-based model of care to support cardiac patients transitioning from hospital to the community (Preprint)

BACKGROUND This is the first randomised controlled trial (RCT) of a mobile health intervention th... more BACKGROUND This is the first randomised controlled trial (RCT) of a mobile health intervention that combines telemonitoring and educational components for both acute coronary syndrome (ACS) and heart failure (HF) inpatients to prevent readmission. OBJECTIVE Objective: To evaluate the feasibility, efficacy and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care – TCC) plus usual care in patients being discharged from hospital after an ACS or HF admission, in comparison to usual care alone. METHODS Methods: In this pilot, 2-centre RCT, a smartphone app-based model of care (TeleClinical Care – TCC) was applied at discharge. The primary endpoint was the incidence of unplanned 30-day readmissions. Secondary endpoints included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weig...

Research paper thumbnail of Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic

Yearbook of Medical Informatics, 2021

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in populari... more Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19.

Methods: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a ‘no-contact’ method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review.

Results: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high.

Conclusion: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.

Research paper thumbnail of The Impact of Age on Smartphone Ownership in a Cardiology Inpatient Population

Hurt, Lung and Circulation, 2020

Introduction The rise of mobile health (mHealth) innovations to improve patient care in cardiolog... more Introduction
The rise of mobile health (mHealth) innovations to improve patient care in cardiology has been dramatic. There is growing interest in the development of smartphone applications, however the utility of these is dependent on the ownership of smartphones within the target population.

Aim
To characterise smartphone ownership rates among four specific age groups, in patients admitted to two metropolitan hospitals for either acute coronary syndrome (ACS) or decompensated heart failure.

Methods
474 patients admitted to the two hospitals were screened for eligibility in a smartphone study (hospital 1 February 2019 to January 2020, October 2019 to January 2020 at Hospital 2). Statistical analysis was performed using the Pearson Chi-Square test.

Results
Mobile phone ownership data was available for 427 patients (90.5%). The mean age was 71.5 years. In patients under the age of 60, the smartphone ownership rate was 90% (81/90). In the patients aged 60-69, the rate was 83.1% (69/83), which was not significantly different to those under the age of 60 (p=0.19). In patients aged 70-79, the rate of smartphone ownership fell to 53.3% (57/107), and was even lower in patients aged 80 and above (23.8%, 35/112). P value for significance overall was <0.001.

Conclusions
87% percent of inpatients under the age of 70 owned smartphones, and this represents a population that could be targeted by mHealth innovations. Smartphone-based interventions may have limited uptake in those aged 70 or above.

Research paper thumbnail of REMOVING BASELINE WANDER FROM ECG SIGNAL USING WAVELET TRANSFORM

REMOVING BASELINE WANDER FROM ECG SIGNAL USING WAVELET TRANSFORM, Sep 4, 2019

Electrocardiogram (ECG) signal is the representation of electrical activity generated by heart mu... more Electrocardiogram (ECG) signal is the representation of electrical activity generated by heart muscles, which is primarily utilized to detect cardiac abnormalities. Due to the sensitive nature of ECG, its important features are affected by different noises and create problems for diagnosis. This study proposes biorthogonal wavelet family by investigating different wavelet families to reduce baseline wander from the ECG signal. The proposed approach performance is compared to adaptive normalized least-mean-square (NLMS) and notch filters. Different performance parameters, such as amplitude spectrum, magnitude squared coherence (MSC), and power spectral density (PSD) has been evaluated. Signal-to-noise ratio (SNR), percentage root-mean-square difference (PRD), mean-square-error (MSE), normalized mean-square-error (NMSE), root mean-square-error (RMSE), and normalized root mean-square-error (NRMSE) performance parameters are calculated as well. The SNR values of the reconstructed ECG signal are-0.0046 dB and 1.6122 dB for notch and adaptive NLMS filters, respectively, which are lower than that of 8.0464 dB for the biorthogonal wavelet transform. Similarly, the MSC values are 0.091903 and 0.44522 after notch and adaptive NLMS filtrations, respectively, which are lower than those of 0.8913 after wavelet filtration. Also, the PSD value for the wavelet transform is-9.317 dB/Hz, which is better than that of adaptive NLMS (-6.788 dB/Hz) and notch (-6.669 dB/Hz) filters. Therefore, the analysis based on performance parameters has justified that proposed biorthogonal wavelet family represent better performance for reducing baseline wander from the ECG signal than adaptive NLMS and notch filters.

Research paper thumbnail of KU-BdSL: An open dataset for Bengali sign language recognition

Data in Brief, 2023

Sign language is a form of communication medium for speech and hearing disabled people. It has v... more Sign language is a form of communication medium for
speech and hearing disabled people. It has various forms
with different troublesome patterns, which are difficult for
the general mass to comprehend. Bengali sign language
(BdSL) is one of the difficult sign languages due to its immense number of alphabet, words, and expression techniques. Machine translation can ease the difficulty for disabled people to communicate with generals. From the machine learning (ML) domain, computer vision can be the
solution for them, and every ML solution requires a optimized model and a proper dataset. Therefore, in this research
work, we have created a BdSL dataset and named ‘KU-BdSL’,
which consists of 30 classes describing 38 consonants (‘banjonborno’) of the Bengali alphabet. The dataset includes 1500
images of hand signs in total, each representing Bengali consonant(s). Thirty-nine participants (30 males and 9 females)
of different ages (21–38 years) participated in the creation
of this dataset. We adopted smartphones to capture the images due to the availability of their high-definition cameras.
We believe that this dataset can be beneficial to the deaf and
dumb (D&D) community. Identification of Bengali consonants
of BdSL from images or videos is feasible using the dataset.
It can also be employed for a human-machine interface for
disabled people. In the future, we will work on the vowels
and word level of BdSL.

Research paper thumbnail of Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care

Frontiers in Medicine, Feb 8, 2022

Discussion: Use of TCC was associated with several benefits, including higher patient engagement ... more Discussion: Use of TCC was associated with several benefits, including higher patient engagement and completion rates with CR. The conduct and delivery of TCC-Cardiac will be improved by the findings of this process evaluation to optimise recruitment, and establishing the roles of GPs and cardiologists as part of the model.

Research paper thumbnail of A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Diagnostics, 2023

Bibliometric analysis is a widely used technique for analyzing large quantities of academic liter... more Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

Research paper thumbnail of Demographic Factors That Influence Smartphone Ownership in a Cardiology Inpatient Population

Heart, Lung and Circulation, 2021

Research paper thumbnail of The Cost-effectiveness of TeleClinical Care: A Telemonitoring and Educational Smartphone App-based Model of Care

Heart, Lung and Circulation, 2021

Research paper thumbnail of Patterns and predictors of smartphone ownership in a cardiology inpatient population

European Heart Journal, 2021

Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chr... more Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chronic disease management. Uptake of these interventions depends on patient smartphone ownership. Purpose To examine the smartphone ownership rate among cardiac inpatients and identify the associated demographic factors. Methods Between February 2019 and March 2020, 565 patients were screened for potential enrolment in the TeleClinical Care (TCC) pilot study at two hospitals in Australia. All patients had an admission diagnosis of acute coronary syndrome or heart failure. Mobile phone ownership was documented at the time of screening. Retrospectively, each patient's electronic medical record was examined for: age, sex, primary diagnosis, suburb of residence, private health insurance subscription, smoking status and occupation. Continuous variables were analysed using a multinomial logistic regression model. Categorical variables were analysed using a generalised linear model. Results M...

Research paper thumbnail of Process Evaluation of the TeleClinical Care Randomised Controlled Trial

Heart, Lung and Circulation, 2021

Research paper thumbnail of Removing power line interference from ECG signal using adaptive filter and notch filter

2014 International Conference on Electrical Engineering and Information & Communication Technology, 2014

Performance of two adaptive filters, such as, normalized least-mean-square (NLMS) adaptive filter... more Performance of two adaptive filters, such as, normalized least-mean-square (NLMS) adaptive filter and recursive-least-square (RLS) adaptive filter are compared with a traditional notch filter both in time and frequency domains to remove the power line interference from the ECG signal. The power spectral density (PSD) and spectrogram analyses are also performed. Different performance parameters, such as, SNR, %PRD and MSE are also calculated. Real time recorded data from the benchmark MIT-BIH arrhythmia database has been used. The result demonstrates superior performance of adaptive NLMS filter for removing power line interference over adaptive RLS and notch filters.

Research paper thumbnail of An intelligent mHealth-based adjunct to improve the management of patients with cardiovascular disease

Regular recording of vital signs, modification of lifestyle behaviour and monitoring of health pr... more Regular recording of vital signs, modification of lifestyle behaviour and monitoring of health progress has been shown to be effective to better manage patients with cardiovascular disease (CVD). Despite this, there remain significant hospital readmissions due to CVD exacerbations. This thesis investigated if the readmission rate of CVD patients could be reduced through remote longitudinal monitoring of physiological measurements and by offering a mobile health (mHealth)-based adjunct to assist in lifestyle modification. The thesis also investigated if there was a relationship between patient engagement and their clinical outcomes.To improve the remote management of CVD patients, the architecture of an intelligent mHealth adjunct called Total Cardiac Care (TCC) was developed based around a smartphone app and wireless peripherals to record physiological data and patient activity. The system also enabled the clinician to regularly monitor the patients’ condition using a web portal, fa...

Research paper thumbnail of A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial

JMIR Mhealth Uhealth, 2022

Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are fr... more Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. Objective: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. Methods: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months.

Research paper thumbnail of Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care

Frontiers in Medicine, 2022

BackgroundA novel smartphone app-based model of care (TeleClinical Care – TCC) for patients with ... more BackgroundA novel smartphone app-based model of care (TeleClinical Care – TCC) for patients with acute coronary syndrome (ACS) and heart failure (HF) was evaluated in a two-site, pilot randomised control trial of 164 participants in Sydney, Australia. The program included a telemonitoring system whereby abnormal blood pressure, weight and heart rate readings were monitored by a central clinical team, who subsequently referred clinically significant alerts to the patients' usual general practitioner (GP, also known as primary care physician in the United States), HF nurse or cardiologist. While the primary endpoint, 30-day readmissions, was neutral, intervention arm participants demonstrated improvements in readmission rates over 6 months, cardiac rehabilitation (CR) completion and medication compliance. A process evaluation was designed to identify contextual factors and mechanisms that influenced the results, as well as strategies of improving site and participant recruitment a...

Research paper thumbnail of TeleClinical Care: A Randomised Control Trial of a Smartphone-Based Model of Care for Patients with Heart Failure or Acute Coronary Syndrome

Heart, Lung and Circulation, 2021

Research paper thumbnail of A randomised control trial of TeleClinical Care – a smartphone-app based model of care for heart failure and acute coronary syndromes

European Heart Journal, 2021

Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalis... more Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalisation and readmissions. A novel smartphone app-based model of care (TeleClinical Care – TCC) was developed to support patients after ACS or HF admission. Purpose This randomised control trial aimed to characterise both the intervention and clinical outcomes. The primary endpoint was the incidence of 30-day readmissions. Secondary endpoints included six-month cardiac and all-cause readmissions, mortality, major adverse cardiovascular events (MACE), cardiac rehabilitation (CR) completion, medication adherence, serum low-density lipoprotein (LDL-C), quality of life, blood pressure, body mass index, waist circumference and six-minute walk distance. Additionally, cost-effectiveness and user satisfaction were evaluated. Methods Patients were randomised 1:1 to either TCC plus usual care or usual care alone and were followed-up at six months. Intervention arm participants received the TCC app an...

Research paper thumbnail of 771 The Impact of Age on Smartphone Ownership in a Cardiology Inpatient Population

Heart, Lung and Circulation, 2020

Research paper thumbnail of Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic

Yearbook of Medical Informatics, 2021

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularit... more Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. Methods: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organizatio...

Research paper thumbnail of Telemedicine systems to manage chronic disease

Digital Health, 2021

The global burden of life-threatening chronic disease continues to increase and is mostly borne b... more The global burden of life-threatening chronic disease continues to increase and is mostly borne by older people. For the last two decades, the practice of telemedicine has focused on a patient-centric approach, which provides greater access to remote medical care at significantly lower cost. However, the manual nature of remote monitoring approaches creates new challenges regarding biosignal analysis and visualization. This chapter will provide an overview of existing issues related to intelligent remote patient monitoring and management. We will highlight the difficulties associated with the acquisition, analysis, interpretation and visualization of critical biosignals for meaningful diagnosis and intervention and suggest some possible pathways to address these problems. Finally, we will discuss potential future directions for telemedicine models based on intelligent decision support systems and data analytics.

Research paper thumbnail of TeleClinical Care: A randomised controlled trial of a smartphone-based model of care to support cardiac patients transitioning from hospital to the community (Preprint)

BACKGROUND This is the first randomised controlled trial (RCT) of a mobile health intervention th... more BACKGROUND This is the first randomised controlled trial (RCT) of a mobile health intervention that combines telemonitoring and educational components for both acute coronary syndrome (ACS) and heart failure (HF) inpatients to prevent readmission. OBJECTIVE Objective: To evaluate the feasibility, efficacy and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care – TCC) plus usual care in patients being discharged from hospital after an ACS or HF admission, in comparison to usual care alone. METHODS Methods: In this pilot, 2-centre RCT, a smartphone app-based model of care (TeleClinical Care – TCC) was applied at discharge. The primary endpoint was the incidence of unplanned 30-day readmissions. Secondary endpoints included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weig...

Research paper thumbnail of Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic

Yearbook of Medical Informatics, 2021

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in populari... more Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19.

Methods: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a ‘no-contact’ method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review.

Results: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high.

Conclusion: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.

Research paper thumbnail of The Impact of Age on Smartphone Ownership in a Cardiology Inpatient Population

Hurt, Lung and Circulation, 2020

Introduction The rise of mobile health (mHealth) innovations to improve patient care in cardiolog... more Introduction
The rise of mobile health (mHealth) innovations to improve patient care in cardiology has been dramatic. There is growing interest in the development of smartphone applications, however the utility of these is dependent on the ownership of smartphones within the target population.

Aim
To characterise smartphone ownership rates among four specific age groups, in patients admitted to two metropolitan hospitals for either acute coronary syndrome (ACS) or decompensated heart failure.

Methods
474 patients admitted to the two hospitals were screened for eligibility in a smartphone study (hospital 1 February 2019 to January 2020, October 2019 to January 2020 at Hospital 2). Statistical analysis was performed using the Pearson Chi-Square test.

Results
Mobile phone ownership data was available for 427 patients (90.5%). The mean age was 71.5 years. In patients under the age of 60, the smartphone ownership rate was 90% (81/90). In the patients aged 60-69, the rate was 83.1% (69/83), which was not significantly different to those under the age of 60 (p=0.19). In patients aged 70-79, the rate of smartphone ownership fell to 53.3% (57/107), and was even lower in patients aged 80 and above (23.8%, 35/112). P value for significance overall was <0.001.

Conclusions
87% percent of inpatients under the age of 70 owned smartphones, and this represents a population that could be targeted by mHealth innovations. Smartphone-based interventions may have limited uptake in those aged 70 or above.

Research paper thumbnail of REMOVING BASELINE WANDER FROM ECG SIGNAL USING WAVELET TRANSFORM

REMOVING BASELINE WANDER FROM ECG SIGNAL USING WAVELET TRANSFORM, Sep 4, 2019

Electrocardiogram (ECG) signal is the representation of electrical activity generated by heart mu... more Electrocardiogram (ECG) signal is the representation of electrical activity generated by heart muscles, which is primarily utilized to detect cardiac abnormalities. Due to the sensitive nature of ECG, its important features are affected by different noises and create problems for diagnosis. This study proposes biorthogonal wavelet family by investigating different wavelet families to reduce baseline wander from the ECG signal. The proposed approach performance is compared to adaptive normalized least-mean-square (NLMS) and notch filters. Different performance parameters, such as amplitude spectrum, magnitude squared coherence (MSC), and power spectral density (PSD) has been evaluated. Signal-to-noise ratio (SNR), percentage root-mean-square difference (PRD), mean-square-error (MSE), normalized mean-square-error (NMSE), root mean-square-error (RMSE), and normalized root mean-square-error (NRMSE) performance parameters are calculated as well. The SNR values of the reconstructed ECG signal are-0.0046 dB and 1.6122 dB for notch and adaptive NLMS filters, respectively, which are lower than that of 8.0464 dB for the biorthogonal wavelet transform. Similarly, the MSC values are 0.091903 and 0.44522 after notch and adaptive NLMS filtrations, respectively, which are lower than those of 0.8913 after wavelet filtration. Also, the PSD value for the wavelet transform is-9.317 dB/Hz, which is better than that of adaptive NLMS (-6.788 dB/Hz) and notch (-6.669 dB/Hz) filters. Therefore, the analysis based on performance parameters has justified that proposed biorthogonal wavelet family represent better performance for reducing baseline wander from the ECG signal than adaptive NLMS and notch filters.

Research paper thumbnail of Managing cardiovascular disease with a smartphone: An intelligent mHealth smartphone app and system for remote monitoring of patients with cardiovascular disease

2019 Postgraduate Research Symposium Program, UNSW, 2019

Cardiovascular disease (CVD) is a growing health concern, especially for older people. In Austr... more Cardiovascular disease (CVD) is a growing health concern, especially for older people. In Australia, it is responsible for high mortality (27% of all deaths in 2017) and hospitalisation (11% of all hospitalisations in 2016-17) rates with a huge economic burden ($10.4 billion in 2015-16). In the last two decades, the practice of telehealth has introduced a patient-centric approach to provide greater access to remote medical care at a significantly low cost. Unfortunately, the manual nature of remote monitoring approaches in patient and clinical care setting creates new challenges to monitor and investigate the critical CVD condition and apply timely interventions. This work describes an intelligent mHealth-based adjunct to improve remote monitoring efficiency and efficacy in both the patient and clinical care settings to deliver cardiac care for remote CVD patients. The front end is based on a smartphone application that can automatically collect and transmit blood pressure, weight, and activity to a secure server, visualise measurements in real-time and deliver notifications and tutorials. The backend is based on smart web portal that can produce emergency alerts and generate interactive graphs, where the clinician can monitor patient condition and suggest required intervention. The feasibility of a prototype of this system has been validated by a previous pilot randomised control trial (N=66) with improved cardiac rehabilitation completion rates (67% vs. 88%, p=0.038). The updated version is currently deployed in a randomised control trial (N=300). Development of a health status prediction model is ongoing and will be later embedded in this system to provide accurate prediction and timely intervention to prevent disease exacerbation. The proposed system has great potential to improve the remote management of CVD patients by reducing mortality and rehospitalisations and improving self-care behaviour.

Research paper thumbnail of Chapter 12 - Telemedicine systems to manage chronic disease

Academic Press, 2021

The global burden of life-threatening chronic disease continues to increase and is mostly borne b... more The global burden of life-threatening chronic disease continues to increase and is mostly borne by older people. For the last two decades, the practice of telemedicine has focused on a patient-centric approach, which provides greater access to remote medical care at significantly lower cost. However, the manual nature of remote monitoring approaches creates new challenges regarding biosignal analysis and visualization. This chapter will provide an overview of existing issues related to intelligent remote patient monitoring and management. We will highlight the difficulties associated with the acquisition, analysis, interpretation and visualization of critical biosignals for meaningful diagnosis and intervention and suggest some possible pathways to address these problems. Finally, we will discuss potential future directions for telemedicine models based on intelligent decision support systems and data analytics.