This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device

Intra-body communication is a novel data transmission method that uses the human body as an electrical channel. The idea is driven by the vision of a cable-free biomedical monitoring system. On-body and implanted sensors monitor the vital functions and transfer data through the human body to a central monitoring unit. Especially for risk patients and long-term applications, such a technology offers more freedom, comfort, and opportunities in clinical monitoring. In this thesis, the human body is characterized as a transmission medium for electrical currents by means of the dielectric properties and the developed electrical models of human tissue. Numerical finite-element simulations are compared to in vivo measurements. For that purpose, a sophisticated measurement hardware has been developed that applies alternating 1 mA peak current in the promising frequency range of 10 kHz to 1 MHz. The individual-specific variations of the transmission characteristics have been investigated in a clinical trial. The subjects’ extra-cellular and intracellular water distribution and skin condition have been identified as the most significant indicators. Overall, the thorax features reasonable transmission characteristics with an averaged attenuation of 55 dB and a typical SNR of 20 dB, while the extremities and joints cause poorer transmissions. Finally, this work proposes transmitter and receiver architectures for intrabody communication. Data transfers of up to 64 kbit/s with BPSK modulation have been achieved through the human body. The first VLSI implementation of a modified SPIHT algorithm offers compression ratios of up to 20:1 for ECG signals still containing all significant details for medical diagnosis. The implementations fulfill all biomedical requirements on galvanic coupling into and within the human body.

Intra-body communication for biomedical sensor networks

An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

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ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System

Wavelet energy based diagnostic distortion measure for ECG

Physiological signals such as ECG signals are obtained from a patient. In accordance with one or more embodiments, an apparatus, system and/or method is directed to at least two ECG sensing electrodes that adhere to remote locations on a patient and sense ECG signals from the patient. An amplifier circuit amplifies the sensed ECG signals to provide amplified ECG signals, and a digitizing circuit digitizes the amplified ECG. A computing circuit processes the digitized ECG signals (e.g., by removing noise). A battery powers the aforesaid circuits, and a housing houses the battery and aforesaid circuit. A fastener mechanically fastens and electrically couples the housing to one of the electrodes, and the other electrodes are coupled to the amplifier via a flexible insulated lead wire.

System for processing physiological data

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.

A 2-D ECG compression method based on wavelet transform and modified SPIHT

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.

Chia-Chun Sun

Papers

A 2-D ECG compression method based on wavelet transform and modified SPIHT

Beat-based ECG compression using gain-shape vector quantization