Development of a Mental Disorder Screening System Using Support Vector Machine for Classification of Heart Rate Variability Measured from Single-lead Electrocardiography

Kobayashi, Mai; Sun, Guanghao; Shinba, Toshikazu; Matsui, Takemi; Kirimoto, Tetsuo

doi:10.1109/sas.2019.8706009

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Reference [11] proposed an MDD diagnosis system using SVM-RFE only on HRV. Reference [12] proposed a single-lead ECG system based on HRV reactivity, ECG signal, and mental tasks analysis, to classify MDD subjects from non-MDD subjects. Most of these studies report promising results.…”

Section: B Hrv Data For Major Depressive Disorder Diagnosismentioning

confidence: 99%

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Yoo¹,

Son²,

Jeong³

et al. 2021

Preprint

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While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clusteringbased FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.

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Section: B Hrv Data For Major Depressive Disorder Diagnosismentioning

confidence: 99%

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Yoo¹,

Son²,

Jeong³

et al. 2021

Preprint

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“…On the other hand, other objective biomarkers have been shown to be useful for physicians to evaluate and assess the level of depression of the patient in a more confident and precise manner. Recent studies have demonstrated the impact of depression on physiological biomarkers, such as heart rate variability (HRV) calculated from the electrocardiogram (ECG) [14] [15], HRV using photoplethysmography (PPG) signals [16] [17], electrodermal activity (EDA) [18] or acoustic physiological features from the speech [19].…”

Section: Introductionmentioning

confidence: 99%

“…PPG is often used to monitor the heart rate (HR) and the blood oxygen saturation (SpO 2 ) but has been widely used in the scientific literature to estimate different physiological parameters such as Heart Rate Variability (HRV). Recent studies have utilized these PPG-derived pa-rameters to detect affective states such as depression or pain [20] [17]. In particular, depression has been clinically found to correlate with parameters on both sympathetic and parasympathetic activity, including autonomic nerve transient responses [16], or the high frequency (HF) and low frequency (LF) components of the HRV [17].PPG signals can be recorded using contact-based medical-graded devices (i.e., fingertip pulse oximeter) or wearable devices such as smartwatches, fitness trackers, or earphones [21].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have utilized these PPG-derived pa-rameters to detect affective states such as depression or pain [20] [17]. In particular, depression has been clinically found to correlate with parameters on both sympathetic and parasympathetic activity, including autonomic nerve transient responses [16], or the high frequency (HF) and low frequency (LF) components of the HRV [17].PPG signals can be recorded using contact-based medical-graded devices (i.e., fingertip pulse oximeter) or wearable devices such as smartwatches, fitness trackers, or earphones [21]. The main advantage of this modality is that it is affordable, non-invasive, and portable.…”

Section: Introductionmentioning

confidence: 99%

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Depression Recognition Using Remote Photoplethysmography From Facial Videos

Casado

Cañellas

López

2023

IEEE Trans. Affective Comput.

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Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.

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“…More particularly, time, frequency, time-scale, and time-frequency domain were investigated in several studies for extraction of some specific features [3][4][5][6][7]. Classification techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN) and decision trees, were used to analyze HRV signals for diagnosis aid purposes [8][9][10][11][12]. These classifiers are applied directly on HRV signals or on features of interest extracted from this signal on different domains.…”

mentioning

confidence: 99%

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

et al. 2022

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In this paper, we have developed a client-server model implemented as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This technical platform capable of detecting and classifying in real-time cardiac arrhythmia via a Graphical User Interface (GUI) using Time-Frequency analysis methods, features extraction, features selection, and classification of the Heart Rate Variability signal recorded using a data acquisition system. The data acquisition system that we used is mainly designed around the Raspberry Pi zero which communicates with a server through the TCP/IP involving a 4G/3G connection secured by the transport layer security (TLS) for a reliably safe connection between client and server. This telemedical platform adopts continuous control and monitoring of the heart rhythm. By using this system, in case of an alarm, medical staff can easily communicate with their patients in the hospital or at home.

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Development of a Mental Disorder Screening System Using Support Vector Machine for Classification of Heart Rate Variability Measured from Single-lead Electrocardiography

Cited by 11 publications

References 12 publications

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Depression Recognition Using Remote Photoplethysmography From Facial Videos

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

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