Field programmable gate array implementation of an adaptive filtering based noise reduction and enhanced compression technique for healthcare applications

Nowadays, the development of e-health monitoring devices is getting more demand for remote health applications. In addition, cardiovascular diseases is considered the most chronic disease in recent years. So, this requires a fast diagnosis and therapy to clear this issue and requires an information transformation from the patient to the distant hospital. But, it faces many challenges like more energy consumption, data security, storage, and transmission. In addition, the electrocardiogram (ECG) data must be processed in real-time, and the information must have high predictability and lossless compression. So, in this article, an enhanced set partitioning in hierarchical tree decoder with deep belief neural network is developed for ECG compression. Due to an inappropriate channel in telemedicine applications, the ECG signals transmitted through a wireless network are affected by noise. So, fast normalized least mean square (FNLMS) algorithm based adaptive filter is deliberated to eliminate the unwanted noise from the ECG signal. Also, the structure of the FNLMs based adaptive filter is modified by developing a systolic structure to increase the speed of filtering process. Moreover, the parallel and pipelined architecture is provided for the proposed compression unit to reduce the processing time and the area overhead as compared to the existing methods. A publicly available database called the MIT-BIH database and real-time ECG data records are used for simulation purposes. The proposed design was implemented on the Xilinx platform and the MATLAB tool. The performance of the proposed design is estimated by the parameters like compression ratio, signal to noise ratio, root mean square error, and percentage root mean square difference (PRD), PRD normalized, and quality-score. It is compared with existing designs to show the efficiency of our proposed design.