• Engr. S.K. Moududul Haque

ECG is a nearly periodic signal that reflects the activity of the heart. This paper presents a computer-based ECG feature extraction system for disease detection. The system provides fundamental features (amplitudes and intervals) for subsequent automatic analysis. Objective: The purpose of this research to develop a computer-based ECG signal analysis system for disease detection. To extract fundamental features (amplitudes and intervals) from ECG signals. To use the extracted features for subsequent automatic analysis of ECG signals. Method: Preprocess the ECG signal to remove noise. Extract the features of interest, such as the amplitude and intervals of the P, Q, R, S, and T waves. Use the extracted features to classify the ECG signal or to diagnose a heart condition. Result: An expert system was developed to diagnose heart diseases from ECG signals. The system uses 8 features to classify 4 types of diseases; with an accuracy of 72%. The system was implemented in MATLAB and tested on the ALRTH arrhythmia database. Conclusion: This paper proposes a system to detect heart conditions from ECG signals. The system uses feature extraction to identify abnormalities in the ECG signal. The system can detect more than 4 types of diseases.

Electrocardiogram (ECG); Heart Rhythm Disturbances; QRS Detection; Wander on the Baseline; Atrial Frequency; Slow Heart Rate

"ECG Signal Preprocessing and Feature Extraction for Diagnosis Using MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e301-e307, September-2023, Available :http://www.jetir.org/papers/JETIR2309436.pdf

"ECG Signal Preprocessing and Feature Extraction for Diagnosis Using MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe301-e307, September-2023, Available at : http://www.jetir.org/papers/JETIR2309436.pdf