mit-bih arrhythmia FFT

MIT-BIH Arrhythmia Database is a widely used dataset for benchmarking electrocardiogram (ECG) classification algorithms. The dataset contains ECG recordings from 47 patients, each lasting from 30 minutes to several hours. One of the most common methods used to analyze ECG signals is the Fast Fourier Transform (FFT).

The FFT is a mathematical algorithm that transforms a time-domain signal into its frequency-domain representation. In ECG analysis, the FFT can be used to extract features such as heart rate variability, which can be indicative of arrhythmias.

To apply FFT on the MIT-BIH Arrhythmia Database, the ECG signals are first preprocessed to remove noise and baseline wander. Then, the FFT is applied to each ECG signal segment to obtain its frequency-domain representation. The resulting FFT coefficients can be further processed to extract relevant features for arrhythmia classification.

Overall, the FFT is a useful tool for analyzing ECG signals in the frequency domain and can be used to aid in the diagnosis of arrhythmias.