A new real-time lossless data compression algorithm for ECG and PPG signals

Banerjee, Soumyendu; Singh, Girish Kumar

doi:10.1016/j.bspc.2022.104127

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…In response to this evident lacuna, a contemporary paradigm has surfaced: an autonomous rehabilitative training framework. This blueprint, meticulously crafted, pivots around the contemporary Human Activity Recognition (HAR) paradigms, with the intent to discern and mentor patients throughout their therapeutic routines [28]. By amalgamating automation's tenets with rehabilitative principles, this framework is on the cusp of transforming the cardiovascular ailment therapeutic landscape, endorsing patient recuperation in a more democratized and economically prudent fashion.…”

Section: Methodsmentioning

confidence: 99%

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

Mukhametkaly,

Momynkulov,

Kurmanbekkyzy

et al. 2023

IJACSA

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In the evolving realm of medical diagnostics, electrocardiogram (ECG) data stands as a cornerstone for cardiac health assessment. This research introduces a novel approach, leveraging the capabilities of a Deep Convolutional Long Short-Term Memory (Conv-LSTM) network for the early and accurate detection of arrhythmias using ECG data. Traditionally, cardiac anomalies have been diagnosed through heuristic means, often requiring intricate scrutiny and expertise. However, the Deep Conv-LSTM model proposed herein addresses the inherent limitations of traditional methods by amalgamating the spatial feature extraction capability of convolutional neural networks (CNN) with the temporal sequence learning capacity of LSTM networks. Initial results derived from a diverse dataset, comprising myriad ECG waveform anomalies, delineated an enhancement in accuracy, reducing false positives and facilitating timely interventions. Notably, the model showcased adaptability in handling the burstiness of ECG signals, reflecting various heart rhythms, and the perplexity inherent in diagnosing subtle arrhythmic events. Additionally, the model's ability to discern longer, more complex patterns alongside transient anomalies offers potential for broader applications in telemetry and continuous patient monitoring systems. It is anticipated that this innovative fusion of CNN and LSTM architectures will usher a paradigm shift in automated arrhythmia detection, bridging the chasm between technology and the intricate nuances of cardiac physiology, thus improving patient outcomes.

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Section: Methodsmentioning

confidence: 99%

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

Mukhametkaly,

Momynkulov,

Kurmanbekkyzy

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Several basic compression methods for numerical data that are based on more complex methods to deal with the exponential growth of data have been developed. One of these methods is delta coding, (14) which depends on storing the difference between successive elements inside the data being compressed. The delta coding method is suitable for time series data generated by IoT systems where the recorded data, for example, temperature data, change slowly.…”

Section: Introductionmentioning

confidence: 99%

Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

El-Hageen¹,

Albalawi²,

Alatwi³

et al. 2022

Sensors and Materials

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Time series data are collected through most of the applications that permeate our lives today. Internet of Things (IoT) sensor data are generated through smart applications and stored in databases. Time series databases require huge storage spaces, as over time they consume a large amount of memory. In this paper, we propose an enhanced compression algorithm for time series data generated by IoT systems that monitor the production of electrical energy by solar panels. The best way to ensure that solar energy systems have high efficiency is to continuously monitor all electrical and environmental factors. However, this requires the collection of enormous quantities of data that can be used to detect defects in the generation of electric energy or in solar panels. As the data must be available for analysis, a lossless compression algorithm is needed. In addition, the compressed data must be in a format that can be queried to perform analysis operations dependent on speed; this means that the decompression of data should not be timeconsuming. Our results showed the high speed of the compression process along with good compression rate (16.6%) after applying the proposed compression algorithm.

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Arithmetic N-gram: an efficient data compression technique

Hassan,

Javed,

Hussain

et al. 2024

Discov Computing

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Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

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A new real-time lossless data compression algorithm for ECG and PPG signals

Cited by 9 publications

References 34 publications

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

Arithmetic N-gram: an efficient data compression technique

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