![fast fourier transform fast fourier transform](https://challengepost-s3-challengepost.netdna-ssl.com/photos/production/software_photos/000/492/467/datas/original.png)
An index vector is also recorded to store the positions of the generated features.Īt the end of the features generation process, the ECG features computed in both sides (the IMD and the programmer) cannot be entirely equal and differences can be identified because the two signals have been collected at different locations in the patient’s body. The programmer and the WISP record a feature vector F R =, respectively. K x i and k y i are quantized and concatenated to obtain a feature f i =, which is indexed by the position i. b.įor each window, a peak detection function and an FFT transformation are implemented in order to return tuples of the form, where k x i is the i th peak index and k y i is the peak value of the FFT coefficients.
![fast fourier transform fast fourier transform](https://i.ytimg.com/vi/zzunpc93rrU/maxresdefault.jpg)
The measured ECG signal is split into windows. The WISP RFID tag and the reader apply the enhanced FFT scheme to generate a set of features from the sampled ECG signal as described below: a. The IMD and the programmer simultaneously measure the ECG signal from the patient, in the frequency domain, for a predefined period of time. The FFT-based key generation can be described as follows: 1. Low frequencies are removed from the obtained signal using FFT and noises are eliminated after the application of the inverse FFT. The decomposed signals are combined to find the resulting transform signal. FFT-based features generation is based on the decomposition of the complex signals to smaller transforms.