Simple and Effective Signal Processing Techniques to Detect Heartbeats from Electrocardiogram

Accurate detection and analysis of heartbeats are important for diagnosing cardiac functions like arrhythmias and tachycardias. The electrocardiogram (ECG) allows the measurement of the electrical activities of the heart. Among the various ECG leads, modified limb lead II (MLII) has shown benefits in effectively differentiating ECG features. Using ECG recordings from the MIT-BIH Arrhythmia Dataset, this study introduces signal processing techniques to detect and analyze heartbeats based on the structure of the QRS complex using the MLII. The proposed method applies a bandpass filter, QRS peak detections, and measurement of the QRS duration to classify each heartbeat as Wide Complex Tachycardia (WCT) or Narrow Complex Tachycardia (NCT). Our results show the average individual accuracy of 95.92±9.08% and the total beat detection accuracy to be 99.44%. This highlights the effectiveness of the introduced signal processing techniques for heartbeat detection, emphasizing its potential for enhanced ECG-based cardiac health monitoring and arrhythmia classification.