A Review of ECG Data Compression Techniques

Singh, Butta; Kaur, Amandeep; Singh, Jaspreet

doi:10.5120/20384-2644

Cited by 30 publications

(15 citation statements)

References 49 publications

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“…These two methods depend on a First-order Interpolation with two Degrees of Freedom (FOI-2DF) algorithm. The Fan algorithm achieves the FOI-2DF without saving all the raw data samples between the last transmitted and the present sample [21].…”

Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

ECG Reduction for Wearable Sensor

Allami

Stranieri

Balasubramanian

et al. 2016

2016 12th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for realtime computation and incremental data arrival. In this paper, we describe a computationally naïve, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10:1 while still maintaining an error rate below 10%.

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Section: Ecg Data Reduction Methodsmentioning

confidence: 99%

ECG Reduction for Wearable Sensor

Allami

Stranieri

Balasubramanian

et al. 2016

2016 12th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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“…Nilai ini menunjukkan bahwa ukuran data kompresi menjadi 6,24 kali lebih kecil dari ukuran data semula. Jika dibandingkan dengan beberapa penelitian sebelumnya (Singh, Kaur, & Singh, 2015) (Mishra & Rath, 2011). Jika diasumsikan dengan detak jantung 80 bpm maka besar data tanpa kompresi untuk satu jam adalah sebesar 9,3 MB, sedangkan besar data dengan kompresi hanya sebesar 1,49 MB.…”

Section: Hasil Dan Pembahasanunclassified

Kompresi Sinyal EKG menggunakan Teknik Parameter Extraction

2019

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ABSTRAKSistem healthcare IoT menyebabkan peningkatan trafik komunikasi dan jumlah penyimpanan data. Elektrokardiogram (EKG) adalah salah satu alat yang berperan penting dalam healthcare IoT. Pasien yang mengalami kelainan jantung perlu dipantau oleh EKG dalam periode waktu lama sehingga menghasilkan data dalam jumlah yang sangat besar. Kompresi data mampu menjadi solusi masalah di atas. Penelitian ini melakukan kompresi sinyal EKG menggunakan metode parameter extraction untuk satu siklus sinyal dari dua belas pasien yang dipilih secara acak. Hasil penelitian menunjukkan bahwa kinerja kompresi baik, ditunjukkan oleh nilai Compression Ratio (CR) 6,24 dan Mean Square Error (MSE) 0,0018.Kata kunci: IoT, EKG, kompresi data, parameter ekstraction. ABSTRACTHealthcare IoT causing higher data communication traffic and storage. Electrocardiogram (ECG) is one of the important device in healthcare IoT. Patient whose have heart abnormality needs ECG monitoring for long period of time, this causing a big data size. Data compression become one of the solutions for this problem. This research focused on data compression using parameter extraction method for one cycle ECG signal from twelve patients.This research has a good result with Compression Ratio (CR) 6,24 and Mean Square Error (MSE) 0,0018.Keywords: IoT, ECG, data compression, parameter extraction

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“…Singh et al [30] analysed other compression techniques, moving from simple one dimensional to more complex two dimensional systems that are used for image compression. This change was instigated by the realisation that by looking at the signal on a beat level, not only were there sample-to-sample (intra-beat) correlations which could be managed by one dimensional compression techniques, but also there were similarities to the signal on a beat-to-beat (inter-beat) basis that could be further compressed.…”

Section: ) Ecg Specific Compressionmentioning

confidence: 99%

A Non-invasive Wireless Monitoring Device for Children and Infants in Pre-Hospital and Acute Hospital Environments

Winderbank-Scott

Barnaghi

2017

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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This paper describes the design and development of a wireless monitoring system for use within a pediatric environment. The current wired methods used to provide noninvasive sensing are not best suited to their end user, and there is a development need for platform independent data transmission. The main goal has been to develop a practical and flexible proof-of-concept prototype suitable for the transmission of sensor data. This prototype consists of an Arduino based multi-input sensor system with wireless transmission, and an Android monitoring station with the facility to rebroadcast the collected data via email/web as a data file. This was achieved using commercially available hardware platforms. The software produced for the Android device allows for full control of the functionality provided by the sensor platform developed on the Arduino system, as well as storing the data within a relational database. The data can also be graphically represented in realtime on the Android device.

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A Review of ECG Data Compression Techniques

Cited by 30 publications

References 49 publications

ECG Reduction for Wearable Sensor

ECG Reduction for Wearable Sensor

Kompresi Sinyal EKG menggunakan Teknik Parameter Extraction

A Non-invasive Wireless Monitoring Device for Children and Infants in Pre-Hospital and Acute Hospital Environments

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