Heart Rate Extraction using Convolutional Neural Network and Long Short-Term Memory


  • Siva Dharshini S L


Rate(HR),Photoplethysmogram,Convolutional Neural Network Long-term Short-term Memory(CNN-LSTM)


Continuous Monitoring the Physiological parameters is crucial for determining the patient’s health status, specially before and after an operation. Heart Rate(HR) is one of the physiological parameters and it is the most important indicators for Cardiovascular diseases. For conventional methods multiple electrodes are attached to the body surface which is difficult and inconvenient for continuous monitoring. To address this need, this paper presents a method for estimating heart rate from the signal of a Photoplethysmography with minimal number of sensor units and making it cost effective. This paper presents a deep learning framework for Heart Rate extraction from Photoplethysmogram (PPG) signal The deep learning framework consists of Convolutional Neural Network and Long Short-Term Memory(CNN-LSTM) that has the ability of feature extraction and efficiently estimate heart rate (HR) information using PPG signal.

Author Biography

Siva Dharshini S L

Department of Electronics and Communication Engineering St. Xavier’s Catholic College of Engineering

Kanyakumari, India