Hugo Posada-Quintero | University of Connecticut (original) (raw)

Papers by Hugo Posada-Quintero

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Biomedical Signal Processing and Control

Journal of Diabetes Science and Technology

Diabetes distal symmetrical peripheral neuropathy (DSPN) is the most prevalent form of neuropathy... more Diabetes distal symmetrical peripheral neuropathy (DSPN) is the most prevalent form of neuropathy in industrialized countries, substantially increasing risk for morbidity and pre-mature mortality. DSPN may manifest with small-fiber disease, large-fiber disease, or a combination of both. This review summarizes: (1) DSPN subtypes (small- and large-fiber disease) with attention to clinical signs and patient symptoms; and (2) technological diagnosis and screening for large- and small-fiber disease with inclusion of a comprehensive literature review of published studies from 2015-present ( N = 66). Review findings, informed by the most up-to-date research, advance critical understanding of DSPN large- and small-fiber screening technologies, including those designed for point-of-care use in primary care and endocrinology practices.

Artificial neural networks are being used in diagnosis support systems to detect different kind o... more Artificial neural networks are being used in diagnosis support systems to detect different kind of diseases. As the design of multilayer perceptron is an open question, the present work shows a comparison between a traditional empirical way and neuroevolution method to find the best architecture to solve the disease detection problem. Tuberculosis and appendicitis databases were employed to test both proposals. Results show that neuroevolution offers a good alternative for the tuberculosis problem but there is lacks of performance in the appendicitis one.

Frontiers in Physiology

Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. Howev... more Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. However, breathing HBO2 by divers and patients increases the risk of central nervous system oxygen toxicity (CNS-OT), which ultimately manifests as sympathetic stimulation producing tachycardia and hypertension, hyperventilation, and ultimately generalized seizures and cardiogenic pulmonary edema. In this study, we have tested the hypothesis that changes in electrodermal activity (EDA), a measure of sympathetic nervous system activation, precedes seizures in rats breathing 5 atmospheres absolute (ATA) HBO2. Radio telemetry and a rodent tether apparatus were adapted for use inside a sealed hyperbaric chamber. The tethered rat was free to move inside a ventilated animal chamber that was flushed with air or 100% O2. The animal chamber and hyperbaric chamber (air) were pressurized in parallel at ~1 atmosphere/min. EDA activity was recorded simultaneously with cortical electroencephalogram (EEG) a...

Informatics in Medicine Unlocked

2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018

Transthoracic bioimpedance (TBI) is a simple method for assessing body fluid accumulations, and a... more Transthoracic bioimpedance (TBI) is a simple method for assessing body fluid accumulations, and as such can be used for early detection of heart failure. In this work, we used a comfortable vest with various electrodes that was worn daily for 5 minutes to measure bioimpedance. Five different electrode types were tested on N = 10 healthy volunteers: Ag/AgCl, textile, copper mesh (CM) carbon black (CB) polydimethylsiloxane (PDMS), Poly(3,4-ethylenedioxythiophene) (PEDOT) textile CB PDMS, and PEDOT salt textile CB PDMS. Inter-subject TBI and ECG tests were performed on all 5 electrode types, while the obtained results were compared to results acquired with textile electrodes. In addition, intra-subject consistencies of TBI measurements were obtained for textile and CM/CB/PDMS electrodes. Acquired TBI measurements from all electrode types were statistically different compared to textile electrodes. CM/CB/PDMS electrodes achieved the highest correlation to the textile electrodes, and the...

An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1... more An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the US. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scales (VAS). This subjectivity complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of ...

In conditions of pressure and temperature associated with immersion in water, humans are more sus... more In conditions of pressure and temperature associated with immersion in water, humans are more susceptible to severe stress, challenging the human physiological control systems. Reliable tools for the assessment of the stress underwater are needed. Electrodermal activity (EDA) is considered a promising alternative for the assessment of the level of stress in humans. EDA is a measure of the changes in conductance at the skin surface related to sweat production. In normal humidity conditions, EDA changes in response to stress in three main ways: the skin conductance level (SCL) is increased, the occurrence of non-specific skin conductance responses (NS.SCRs) increases, and the normalized spectral power in the band from (EDASympn) 0.045 to 0.25 Hz is elevated. When skin is immersed in water, the humidity blocks the sweat glands, changing the dynamics of EDA. For this reason, we have tested the measures of EDA for subjects immersed in water, as response to cognitive stress. Four subjects...

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of symp... more The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. U...

Computer Methods and Programs in Biomedicine

Attention, Perception, & Psychophysics

We have performed a direct comparison between facial features obtained from a webcam and vigilanc... more We have performed a direct comparison between facial features obtained from a webcam and vigilance-task performance during prolonged wakefulness. Prolonged wakefulness deteriorates working performance due to changes in cognition, emotion, and by delayed response. Facial features can be potentially collected everywhere using webcams located in the workplace. If this type of device can obtain relevant information to predict performance deterioration, this technology can potentially reduce serious accidents and fatality. We extracted 34 facial indices, including head movements, facial expressions, and perceived facial emotions from 20 participants undergoing the psychomotor vigilance task (PVT) over 25 hours. We studied the correlation between facial indices and the performance indices derived from PVT, and evaluated the feasibility of facial indices as detectors of diminished reaction time during the PVT. Furthermore, we tested the feasibility of classifying performance as normal or impaired using several machine learning algorithms with correlated facial indices. Twenty-one indices were found significantly correlated with PVT indices. Pitch, from the head movement indices, and four perceived facial emotions-anger, surprise, sadness, and disgust-exhibited significant correlations with indices of performance. The eye-related facial expression indices showed especially strong correlation and higher feasibility of facial indices as classifiers. Significantly correlated indices were shown to explain more variance than the other indices for most of the classifiers. The facial indices obtained from a webcam strongly correlate with working performance during 25 hours of prolonged wakefulness.

IEEE Access

We performed machine learning for text-independent speaker identification using speech recorded d... more We performed machine learning for text-independent speaker identification using speech recorded during the day, evening, and night, from subjects undergoing 25 hours of prolonged wakefulness. Subjects answered casual questions lasting approximately 3 minutes and described pictures presented to them for 0.5 minutes. We extracted 12,515 vocal features using OpenSmile software. For generalization of the training scheme, we segmented the 20 subjects into training and testing sets (10 subjects for each) and repeated testing four times with different subsets. Specifically, we used one set of 10 subjects to find the best feature-sets and the optimal machine-learning method, and the other set of 10 subjects was used to test the trained model. With trained machine-learning models using three speech sessions recorded throughout the day for speaker identification, we obtained 95% and 98.8% for balanced accuracies for daytime and evening speech, respectively, but 84.2% for nighttime-testing speech. With training data from all times of day-daytime, evening, and nighttime-we obtained 97.5%, 98.8%, and 98.1% for balanced accuracies for test data from daytime, evening, and nighttime speech, respectively; the overall accuracy was 98.1%. Prolonged wakefulness deteriorates the performance of machine-learning based speaker identification. This work suggests that machine-learning based speaker identification should be trained using speech data from both daytime and nighttime speech sessions for better overall accuracy. Machine learning can potentially be used for identifying a speaker's voice even when it is affected by tiredness and fatigue which are frequently encountered in scenarios such as the emergency rooms and long-duration repetitive task operations.

Revista Facultad de Ingeniería

This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepst... more This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems.

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

We developed an objective real-time pain detection method using a smartphone and a wrist-worn wea... more We developed an objective real-time pain detection method using a smartphone and a wrist-worn wearable device to collect electrodermal activity (EDA) signals. Recently, various researchers have developed pain management applications. However, they rely on subjective self-reported pain scores or the video camera of a smartphone to detect pain, but the latter method’s accuracy needs further improvement. In our work, we use a wrist-worn EDA device which transmits data via Bluetooth to a smartphone. A smartphone application was developed to analyze the EDA data so that near real-time processed pain detection information can be displayed. The analysis of EDA is based on estimating time-varying spectral power in the frequency range (0.08-0.24 Hz) associated with the sympathetic nervous system. This time-varying characterization of EDA is termed TVSymp. In this work, we also examined whether removing baseline EDA fluctuations from TVSymp would provide more accurate results. This was carried out by taking the moving average of the EDA response prior to stimulus and subtracting that value from the EDA response post stimulus. This approach is termed modified TVSymp (MTVSymp). Pain stimuli were induced in ten subjects using a thermal grill, which gives intense pain perception without damaging skin tissues. We compared both TVSymp and MTVSymp in detecting pain induced by the thermal grill using machine learning approaches. We found the accuracy of pain detection of TVSymp and MTVSymp to be 80% and 90%, respectively.

Frontiers in Artificial Intelligence, Apr 7, 2020

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilan... more High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is "normal" or "impaired" with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.