Physiological signal analysis for patients with depression (original) (raw)

Survey on Different Approaches of Depression Analysis

2020

Clinical depression has been a common but a serious mood disorder nowadays affecting people of different age group. Since depression affects the mental state, the patient will find it difficult to communicate his/her condition to the doctor. Commonly used diagnostic measures are interview style assessment or questionnaires about the symptoms, laboratory tests to check whether the depression symptoms are related with other serious illness. With the emergence of machine learning and convolutional neural networks, many techniques have been developed for supporting the diagnosis of depression in the past few years. Since depression is a multifactor disorder, the diagnosis of depression should follow a multimodal approach for its effective assessment. This paper presents a review of various unimodal and multimodal approaches that have been developed with the aim of analyzing the depression using emotion recognition. The unimodal approach considers either of the attributes among facial ex...

Different Parameters for Computational Depression Analysis: A Review

2018

Depression is a mental disorder that affects not only the thoughts but also the body and behavior of the person suffering from it. Depression detection, as of today, is limited to analysis of the patient by the psychologist, which is prone to subjective bias. Although depression is a mental disorder, it affects various physical attributes like eye movements, voice modulation, urine, saliva, etc. These effects, if observed and analyzed, can be used for designing tests wherein the physiological parameters are observed and depression is detected objectively, thus reducing the burden on psychologists. This paper gives a systematic review of the studies undertaken for analyzing the effects of depression on different physical attributes.

Depression Evaluation via Heart Rate Variability and Body Temperature

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 2022

Mental status tasks influence several neurophysiological measures. Biomedical instrumentation is widely used to measure the behavior of the human body and calculate the relative physiological responses to cognitive tasks. There is a common connection between heart rate variability (HRV) and ANS activity. In addition, the skin conductance peak characteristics (SC) from electrodermal activity (EDA) and skin temperature (SKT) modifications can also affect ANS activity As such, the autonomic nervous system (ANS) can easily influence depression. Previous efforts to study and apply HRV features as biomarkers of depression are encouraging. This study includes HRV analysis and temperature measurements during a depression task and is designed to explore the connection between electrocardiography (ECG) and body temperature. Regarding HRV analysis, previous research has shown a decrease in high-frequency (HF) features, as well as body temperature decreases during the day, in patients with depression. Five healthy college students with no health issues participated in the study. An ECG was recorded while relaxing and while performing the Stroop Color-Word task; body temperatures were recorded periodically. Results showed that there were six significant relationships between HRV features and body temperature associated with depression. In addition, short-term meditation had a positive influence, and this protocol could be useful in depressive disorders.

IRJET- SURVEY ON DIFFERENT APPROACHES OF DEPRESSION ANALYSIS

IRJET, 2020

Clinical depression has been a common but a serious mood disorder nowadays affecting people of different age group. Since depression affects the mental state, the patient will find it difficult to communicate his/her condition to the doctor. Commonly used diagnostic measures are interview style assessment or questionnaires about the symptoms, laboratory tests to check whether the depression symptoms are related with other serious illness. With the emergence of machine learning and convolutional neural networks, many techniques have been developed for supporting the diagnosis of depression in the past few years. Since depression is a multifactor disorder, the diagnosis of depression should follow a multimodal approach for its effective assessment. This paper presents a review of various unimodal and multimodal approaches that have been developed with the aim of analyzing the depression using emotion recognition. The unimodal approach considers either of the attributes among facial expressions, speech, etc. for depression detection while multimodal approaches are based on the combination of one or more attributes. This paper also reviews several depression detection using facial feature extraction methods that use eigenvalue algorithm, fisher vector algorithm, etc. and speech features such as spectral, acoustic feature, etc. The survey covers the existing emotion detection research efforts that use audio and visual data for depression detection. The survey shows that the depression detection using multimodal approach and deep learning techniques achieve greater performance over unimodal approaches in the depression analysis.

Emotional Status Determination using Physiological Parameters Data Set

2020

This dataset is created with the usage of Galvanic Skin Response Sensor and Electrocardiogram sensor of MySignals Healthcare Toolkit. MySignals toolkit consists of the Arduino Uno board and different sensor ports. The sensors were connected to the different ports of the hardware kit which was controlled by Arduino SDK.MySignals is a development platform for medical devices and e-Health applications. It is a multichannel physiological signal recorder which measures more than 15 different biometric parameters such as pulse, breath rate, oxygen in blood, electrocardiogram signals, blood pressure, muscle electromyography signals, glucose levels, galvanic skin response, lung capacity, snore waves, patient position, airflow and body scale parameters (weight, bone mass, body fat, muscle mass, body water, visceral fat, Basal Metabolic Rate and Body Mass Index).This novel dataset can be applied for training and evaluating deep learning, machine learning and big data analytic models to deal w...

Depression Analysis using ECG Signal

ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons.

An Automated Framework for Depression Analysis

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013

This project aims at developing an automated framework for depression detection. During a depressive episode, patients suffer from psychomotor retardation and this phenomenon is not only limited to facial activity. In this PhD work, it is hypothesized that such complex affective state can be better represented by integrating information from various uni-modal channels to form a multimodal affective sensing system. The project explores facial dynamics, body expressions such as head movement, relative body part movement etc. in patients with major depressive disorders. The contribution of various channels is assessed and as a final objective, a framework combining discriminative channels for automatic depression analysis is proposed.

Digital processing of affective signals

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)

A ective signal processing algorithms were developed to allow a digital computer to recognize the a ective state of a user who is intentionally expressing that state. This paper describes the method used for collecting the training data, the feature extraction algorithms used and the results of pattern recognition using a Fisher linear discriminant and the leave one out test method. Four physiological signals, skin conductivity, blood volume pressure, respiration and an electromyogram EMG on the masseter muscle were analyzed. It was found that anger was well di erentiated from peaceful emotions 90-100, that high and low arousal states were distinguished 80-88, but positive and negative v alence states were di cult to distinguish 50-82. Subsets of three emotion states could be well separated 75-87 and characteristic patterns for single emotions were found.

Investigation of Heart Rate Variability using Wavelet Packet Transform in Major Depressive Disorder

Depression is a common mood disorder that is characterized by impairment of mood regulation, and loss of interest in enjoyable activities. According to the previous studies, it has been reported that this disorder is related with elevated rates of cardiovascular morbidity and mortality. Therefore, as an important indicator for diagnosis and classification of cardiac dysfunctions, heart rate variability (HRV) has been widely used in depression. Differ from the previous studies in this field, wavelet packet transform (WPT) is used for determination of effective very low frequency (VLF), low frequency (LF), and high frequency (HF) bands in HRV signals of depressed patients in this study. Twenty patients who met the DSM-IV criteria for major depressive disorder and age, gender-matched twenty healthy controls were participated for this study. HRV data of these participants were first were recorded using the Brainamp ExG data acquisition system and then decomposed into sub-bands including VLF, LF, HF using WPT with 9 level Daubechies (db4) family and variations of energy in these bands were analyzed in MATLAB. The HRV measures as each sub-band average energy and sympathovagal balance (LF/HF ratio) were compared statistically between patients and controls. The results of this study indicates that especially the mean energy values of sub-frequency ranges in VLF band for each participant are higher than that the values of other bands as LF and HF. In addition, the mean energy values of the regions in LF band of control subjects are significantly lower than the same measure of patients. In contrast, in comparison with control subjects, patients with major depression exhibited low HF band energy. Finally, results indicate that sympathovagal balance that reflects the equilibrium between sympathetic and parasympathetic activity of the autonomic nervous system in patients was higher than that of control subjects indicating autonomic dysfunction throughout the entire experiment. It can be conclude that low cardiovagal activity in patients with major depression may contribute to the higher cardiac dysregulations of these patients.