An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform

Kerdjidj, Oussama; Amira, Abbes; Ghanem, Khalida; Ramzan, Naeem; Katsigiannis, Stamos; Chouireb, Fatima

doi:10.1016/j.micpro.2019.03.007

Cited by 20 publications

(4 citation statements)

References 36 publications

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“…This comparison considered the subsequent attributes: Slice flip flop, LUT, DSP cores, block RAMs, and power (W). Here, the match pursuit algorithm 37 utilizes a low cost Zynq Soc (Zybo board) whose frequency is very low (ie, 33 MHz). This design reduced the energy consumption by employing low frequency realization.…”

Section: Simulation Results and Explanationsmentioning

confidence: 99%

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

Aruna

Ekambaram

Padmaja

2022

Trans Emerging Tel Tech

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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.

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Section: Simulation Results and Explanationsmentioning

confidence: 99%

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

Aruna

Ekambaram

Padmaja

2022

Trans Emerging Tel Tech

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“…Certain researches that are carried out by implementing hardware through IoT platform is discussed in this section. Low-complexity Field Programmable Gate Array (FPGA) hardware implementation was proposed for healthcare applications by Oussama Kerdjidj et al in [233] in which the design was based on pipeline optimization of the Programmable Logic (PL). Simulation of MP algorithm was carried out by Vivado tools, and MATLAB.…”

Section: Cs Realtime Implementations and Iot Frameworkmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

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“…In [2], cooperative techniques are exploited to enhance the coverage and the reliability of WBANs. Interference cancellation in WBANs is targeted in [3], while [4] introduces an FPGA-based realistic solution to reduce the implementation complexity by recoursing to compressed sensing tools. Recently, the nanotechnology and the subsequent possibilities it opens for the reduction of the devices size to the nanoscale dimension, in addition to the research community interest for the adequate nano materials for such devices, such as the graphene, have made conceivable the adoption of the nano-networks for the THz communication within the human body.…”

Section: Introductionmentioning

confidence: 99%

Integration of Spatial Modulation Scheme With Code Division Multiple Access for VIVO Based Frequency Selective Nano Sensor Networks

et al. 2022

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Due to their suitability for human-body based communications, in-VIVO nano sensor networks are envisioned to use the promising Terahertz signals in order to ensure the forthcoming high-rate communication needs of the modern medicine. However, the propagation losses at these frequency bands are quite significant and dependent on the operation frequency as well as on the physiological characteristics, thus impeding the use of Terahertz rates to their utmost benefit. Using numerous emitting elements is likely to improve the quality of the received signal, but gives rise to multi-channel interference (MCI) emanating from multi-antenna signaling reception, which necessitates a relatively complex signal processing to mitigate it. When multiple physiological signals are of interest, detecting them necessitates to mitigate multipleaccess interference (MAI). In this perspective, orthogonal frequency division multiplexing (OFDM) allows to combat the channel frequency selectivity, whereas code division multiple access (CDMA) scheme cancels MAI. In this paper, we propose to embed the novel spatial modulation technique with CDMA architecture in an OFDM framework to ensure a viable communication in in-VIVO frequency selective Nano channels. The immunity of our proposed solution to such an interference is confirmed, since less than -2 dB in SNR level is required to support 5 users simultaneously communicating with a BER which is inferior to 10 −3 when the operating frequency is equal to 1THz. This hybrid scheme is shown to efficiently combat the MCI while enabling a safe retrieval of the useful signal at the very-high data rate communications.

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An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform

Cited by 20 publications

References 36 publications

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

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

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Integration of Spatial Modulation Scheme With Code Division Multiple Access for VIVO Based Frequency Selective Nano Sensor Networks

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