VLSI Implementation of Multi-Channel ECG Lossless Compression System

Tsai, Tsung‐Han; Tung, Nai-Chieh; Lin, Ding-Bang

doi:10.1109/tcsii.2021.3071757

Cited by 8 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be applied even in different ECG signal acquisition setups, i.e., different numbers of records, different time duration per record, different sampling frequencies, or different resolutions (number of bits per sample). For the PTBDB data set, the result in [4,5] showed CR of 3.98 and 4.07, which is higher than the 3.16 achieved by the proposed method. However, we cannot really compare them because, in [4], data from other channels were required to reconstruct the data in the channel of interest.…”

Section: Discussioncontrasting

confidence: 58%

“…Average CR [4] Multi-channel ALP (1st, 2nd, and 4th order weight average) + Golomb-Rice Coding 3.98 (Multi-lead) To evaluate the data compression performance of the proposed method to other stateof-the-art methods, its compression ratios achieved are compared to the related works for PTBDB, EDB, and MITDB data sets, as shown in Tables 1-3, respectively.…”

Section: Ref Compression Methodsmentioning

confidence: 99%

“…Average CR [3] ALP + Context-based Golomb-Rice Coding 2.835 [4] ALP + Golomb-Rice Coding 2.89 [6] SSLMS ALP + Savitzky-Golay Filter 2.15 [7] ALP + Fixed-length Coding 2.25 [8] LP + Burrow-Wheeler Inversion Coding 4.24 [9] Fuzzy-based ALP + 2-stage Huffman Coding 2.53 [10] Dual-mode LP + Error Modeling + Golomb-Rice Coding 2.975-3.040 [11] Takagi-Sugeno fuzzy NN Predictive Coding 3.22 [12] Wavelet-based Vector Quantization 3.031 [14] RAKE Coding Algorithm 2.67 [15] K-means Cluster + Differential Coding 2.49 (12-bit Length) Proposed Prediction Error-based ALP + Golomb-Rice Coding 3.525…”

Section: Ref Compression Methodsmentioning

confidence: 99%

“…Tsai and Kuo [3] proposed the adaptive linear prediction with content-adaptive Golomb-Rice coding. Tsai and Tsai [4] proposed the multi-channel adaptive linear prediction with a modified Golomb-Rice coding method, and Tsai et al [5] also implemented the method in [4] on Very Large-Scale Integration (VLSI). This work compressed the 12-lead ECG signal by using four reference leads to calculate the other eight leads of the signals.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Bannajak

Theera-Umpon

Auephanwiriyakul

2023

IJERPH

View full text Add to dashboard Cite

In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb–Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

show abstract

Section: Discussioncontrasting

confidence: 58%

Section: Ref Compression Methodsmentioning

confidence: 99%

Section: Ref Compression Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Bannajak

Theera-Umpon

Auephanwiriyakul

2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…Tsai et al 27 presented a hardware structure for multichannel linear prediction and adaptive linear prediction based lossless ECG compression system. Here, the entropy coding was performed using Golomb rice code (GRC).…”

Section: Some Of the Recent Related Work Are Given Belowmentioning

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

View full text Add to dashboard Cite

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.

show abstract

A Hybrid Lossless ECG Compressor with Morphological Detection and QRS Prediction

Tang,

Guo,

Zhang

2024

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

VLSI Implementation of Multi-Channel ECG Lossless Compression System

Cited by 8 publications

References 13 publications

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

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

A Hybrid Lossless ECG Compressor with Morphological Detection and QRS Prediction

Contact Info

Product

Resources

About