Scipy based R peak detection for bradycardia and tachycardia detection

Heart disease or cardiovascular disease is the deadliest disease in the world which claims about 17.9 million lives globally every year. To solve the issues, it needs to improve people’s lifestyles by providing smart reminders or self-diagnosis actions using wearable technology and health applications. The ECG (electrocardiogram) is created to treat cardiovascular diseases. In the study, ECG (electrocardiogram) algorithm will be developed for detecting bradycardia and tachycardia will be developed using Python programming. The target of this study is to find the BPM (beat per minute) of ECG using R peak-based algorithm which will be compared with the data from PhysioNet. The methodology that will be done in this study is to gather the 12 CUDB (Creighton University Ventricular Tachyarrhythmia Database) patients from the PhysioNet, the sampling frequency that will be used is 250 Hz, and the next will filter the ECG data, determine R peak detection & BPM detection using a simple algorithm, calculate the percent error, and confusion matrix. Based on the results, the developed algorithm can detect bradycardia and tachycardia with the help of amplitude and frequency parameters, and the results obtained from the confusion matrix state that they have an excellent result.