Machine Learning Approach on Photoplethysmogram Morphology for Psychiatric Disorders Prediction-Reference-Cited by-同舟云学术

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Machine Learning Approach on Photoplethysmogram Morphology for Psychiatric Disorders Prediction

Published:2022-12-27 Issue: Volume: Page:
ISSN:
Container-title:2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)
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Author:

Awang Azwani¹,Nayan Nazrul Anuar¹,Jaafar Nik Ruzyanei Nik²,Suboh Mohd Zubir¹,Rahman Khairul Anuar A³,Ismail Siti Nor Ashikin¹

Affiliation:

1. Universiti Kebangsaan Malaysia, UKM Bangi,Dept. of Electrical, Electronic and Systems Engineering,Malaysia

2. Hospital Canselor Tuanku Muhriz,Dept. of Psychiatry,WP Kuala Lumpur,Malaysia

3. Universiti Tekninal Malaysia Melaka,Dept. of Electronics & Computer Engineering Technology,Malacca,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10018924/10018967/10019188.pdf?arnumber=10019188

Reference43 articles.

1. Heart rate variability as biomarker for bipolar disorder;stautland;medRxiv,2022

2. Heart Rate Variability as Indicator of Clinical State in Depression

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

4. Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

5. Analysis of heart rate variability during auditory stimulation periods in patients with schizophrenia

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