What question did this study set out to answer?

The study aims to develop an autonomous monitoring system for detecting cardiac anomalies using photoplethysmography.

April 25, 2026Biomedical Signal Processing and Control1 citationsOpen Access

Real-time embedded detection of bradycardia, tachycardia, and arrhythmia using photoplethysmography and on-device convolutional neural networks

AFAmjed Al Fahoum MAMuhannad Ababneh MQMusab Al Qwaider

Key Result

An on-device convolutional neural network using photoplethysmography detected bradycardia, tachycardia, and arrhythmia with 98.57% accuracy and a 500 ms reaction time across 640 subjects.

Key Points

The study aims to develop an autonomous monitoring system for detecting cardiac anomalies using photoplethysmography.
Designed a photoplethysmography-based heart monitoring system integrating a custom acquisition module and STM32F407 microcontroller.
Implemented a real-time convolutional neural network optimized for on-device processing.
Evaluated the system on data from 640 subjects, including 320 with normal rhythms and 320 with cardiovascular conditions.
Achieved 98.57% accuracy in detecting cardiac anomalies.
Demonstrated a 500 ms reaction time with high recall, specificity, and F1-scores.
Outperformed comparable models in experimental evaluations.

Structured PICO

Does a PPG-based real-time heart health monitoring system with on-device RT-CNN accurately detect bradycardia, tachycardia, and arrhythmia?

Population

640 subjects (320 normal and 320 patients with diagnosed cardiovascular conditions)

Intervention

Photoplethysmography (PPG)-based, real-time heart health monitoring system using a custom PPG acquisition module, an STM32F407 microcontroller, and a real-time convolutional neural network (RT-CNN)

Outcome

Detection accuracy of normal rhythms and multiple cardiac anomalies (bradycardia, tachycardia, and arrhythmia)surrogate

A novel on-device PPG-based RT-CNN system provides highly accurate (98.57%) and rapid real-time detection of cardiac arrhythmias without requiring cloud connectivity.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the need for accurate, accessible, and continuous cardiac monitoring solutions. This study presents the design and implementation of a photoplethysmography (PPG)-based, real-time heart health monitoring system that integrates specialized hardware, embedded computing, and artificial intelligence for fully autonomous operation. Unlike traditional approaches requiring external processing or cloud connectivity, the system uses a custom PPG acquisition module, an STM32F407 microcontroller, and a compact real-time convolutional neural network (RT-CNN) optimized for on-device execution. The hardware ensures robust signal acquisition under diverse conditions, while the RT-CNN processes one-dimensional PPG signals to detect normal rhythms and multiple cardiac anomalies with high accuracy. Experimental evaluation demonstrated 98.57% accuracy, a 500 ms reaction time, and consistently high recall, specificity, and F1-scores, outperforming comparable models. Open-source, modular architecture makes the platform scalable for telemedicine, home-based care, and resource-limited settings, while Grad-CAM visualizations enhance clinician trust in AI-assisted decisions. The system was evaluated on data collected from 640 subjects (320 normal and 320 patients with diagnosed cardiovascular conditions). This work offers a cost-effective, portable, and clinically relevant approach to real-time cardiac anomaly detection, addressing key limitations in existing PPG-based monitoring systems.

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