Electrocardiogram Signal Compression Research Articles

A multi-channel ECG signal deep compressive sensing method using Treeshaped Autoecoder based on multiscale feature fusion

Conventionally, multi-lead Electrocardiogram (ECG) signals are recorded and stored using high sensing rates and high precision, resulting in huge data volumes and placing greater pressure on storage and transmission resources. Compressive sensing (CS) allows efficient encoding and decoding of signals through sparse representation, measurement and reconstruction for compressed storage and transmission of ECG signals. Traditional CS methods generally requires manually selecting the features or dictionaries used in sparse representations, which lacks adaptivity and flexibility, and the complexity of the reconstruction process limits the application of CS in some scenarios with high real-time requirements. In this paper, we propose a deep compressive sensing framework for processing multi-lead ECG signals by combining CS and deep learning, which is based on multi-scale feature fusion to construct a binary tree-shaped autoencoder architecture to achieve efficient compression and reconstruction of ECG signals. Experiments on the PTB diagnostic ECG database show that the proposed method achieves “very good” reconstruction quality at low sensing rates, with PRD and SNR of 1.77 % and 35.67 dB, respectively, when SR = 20 %. In addition, we investigate how the number of quantization bits affects the quality of reconstructed signals. After analysis, we found that 8-bit quantization can be used in practical applications to ensure the quality of reconstructed ECG signals, while decrease the bit rate of the data, improving the transmission efficiency, and reduce energy consumption.

Compressed sensing for ECG signal compression using DWT based sensing matrices

ABSTRACT In this article, we have investigated the 1-D discrete wavelet transform (DWT)-based measurement matrices for electrocardiogram (ECG) compression. Moreover, the current work examines the suitability of the diverse DWT matrices, namely Symlets, Battle, Coiflets, Vaidyanathan, and Beylkin wavelet families, for ECG compression. Furthermore, this article shows the comparative performance study of the proposed DWT matrices with conventional deterministic and random measurement matrices. Overall, the Battle1 wavelet-based measurement matrices demonstrate the enhanced performance against the db3, coif5, and sym6 based measurement matrices in terms of Percentage Root-Mean Squared Difference (PRD), Root Mean Square Error (RMSE), and Signal-to-Noise Ratio (SNR). Finally, it was seen that the proposed Battle1 matrix demonstrates the improved performance against the conventional measurement matrices such as the Karhunen–Loeve transform (KLT), Discrete Cosine Transform (DCT) matrix, and random Hadamard measurement matrix. Thus, the result shows the adequacy of DWT measurement matrices for the compression of ECG.

Electrocardiogram signal compression using adaptive tunable-Q wavelet transform and modified dead-zone quantizer

The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and Sparse-PSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD1), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS1). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively.

Compression of an ECG Signal Using Mixed Transforms

Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG datavolume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the ECG data in the 2-D form. The compression algorithms were implemented and tested using multiwavelet, wavelet and slantlet transforms to form the proposed method based on mixed transforms. Then vector quantization technique was employed to extract the mixed transform coefficients. Some selected records from MIT/BIH arrhythmia database were tested contrastively and the performance of theproposed methods was analyzed and evaluated using MATLAB package. Simulation results showed that the proposed methods gave a high compression ratio (CR) for the ECG signals comparing with other available methods. For example, the compression of one record (record 100) yielded CR of 24.4 associated with percent root mean square difference (PRD) of 2.56% was achieved.

Lightweight electrocardiogram signal compression

Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix’s entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

An effective ECG signal compression algorithm with self controlled reconstruction quality

Electrocardiogram (ECG) signals are frequently used in the continuous monitoring of heart patients. These recordings generate huge data, which is difficult to store or transmit in telehealth applications. In the above context, this work proposes an efficient novel compression algorithm by integrating the tunable-Q wavelet transform (TQWT) with coronavirus herd immunity optimizer (CHIO). Additionally, this algorithm facilitates the self-adaptive nature to regulate the reconstruction quality by limiting the error parameter. CHIO is a human perception-based algorithm, used to select optimum TQWT parameters, where decomposition level of TQWT is optimized for the first time in the field of ECG compression. The obtained transform coefficients are then thresholded, quantized, and encoded to improve the compression further. The proposed work is tested on MIT-BIH arrhythmia database. The compression and optimization performance using CHIO is also compared with well-established optimization algorithms. The compression performance is measured in terms of compression ratio, signal-to-noise ratio, percent root mean square difference, quality score, and correlation coefficient.

A Novel Method for ECG Signal Compression and Reconstruction: Down-Sampling Operation and Signal-Referenced Network

Objective. The wearable electrocardiogram (ECG) monitoring device is an effective tool for diagnosing intermittent heart diseases. However, the massive amount of ECG data increases power consumption during wireless transmission, thereby reducing the monitoring duration of the wearable device. Here, we presented a novel method to solve this problem. Method. To reduce the data size, we used a down-sampling operation to compress signals. Then, we designed a signal-referenced network to reconstruct the original signals from the compressed ones. We validated the proposed method on the China Physiological Signal Challenge 2018 database, used the root mean square error (RMSE) to evaluate the performance of the proposed network, and evaluated the effectiveness of the reconstructed signals via the F1-score of an ECG signal classifier. Main Result. The classifier used in this paper achieved an F1-score of 84% on 500 Hz signals reconstructed from 25 Hz, 89% from 50 Hz, 90% from 125 Hz, and 95% from 250 Hz. The RMSE of these four sampling rates was 0.10 mV, 0.08 mV, 0.05 mV, and 0.04 mV, respectively. Significance. The experimental result shows that the proposed network has a good performance when reconstructing signals. Furthermore, our method can remove the computational load of compressing signals from wearable devices.

Optimized Tunable-Q Wavelet Transform-Based 2-D ECG Compression Technique Using DCT

Electrocardiogram (ECG) signals are one of the fundamental health indicators used in the continuous monitoring and diagnosis of severe heart diseases. The acquisition of long-term ECG signals generates large data that negatively affect the proficiency of the transmission channel and storage devices. In this regard, this work presents a novel compression algorithm by combining discrete cosine transform (DCT) and optimized tunable-Q wavelet transform (TQWT) for compression of 2D ECG signals. The proposed algorithm is applied to columns and rows of 2D ECG array, which improves the compression by increasing sparsity in the transform domain. Thereafter, transform coefficients are approximated using an optimized midtread quantizer. TQWT and quantizer’s parameters both are optimized using a recently developed COOT bird optimization algorithm, which simulates the behavior of COOT birds. The obtained quantized coefficients are encoded by adaptive Huffman encoding. The investigational work is tested on MIT-BIH arrhythmia database. The optimization performance of COOT algorithm is also validated by comparing it with some existing algorithms. Similarly, compression performance is also compared with the state-of-art compression techniques in terms of compression ratio, signal-to-noise ratio, percent root-mean-square difference, and quality score. The obtained average values of these parameters are 30.7826, 27.7073 dB, 4.5162 %, and 8.1349, respectively. Attained results strengthen the applicability of proposed method in telemedicine or mobile health-based monitoring systems and memory devices.

Bidirectional Recurrent Nets for ECG Signal Compression

Electrocardiogram (ECG) is a commonly used tool in biological diagnosis of heart diseases. ECG allows the representation of electrical signals which cause heart muscles to contract and relax. Recently, accurate deep learning methods have been developed to overcome manual diagnosis in terms of time and effort. However, most of current automatic medical diagnosis use long electrocardiogram (ECG) signals to inspect different types of heart arrhythmia. Therefore, ECG signal files tend to require large storage to store and may cause significant overhead when exchanged over a computer network. This raises the need to come up with effective compression methods for ECG signals. In this work, the authors investigate using BERT (Bidirectional Encoder Representations from Transformers) model, which is a bidirectional neural network that was originally designed for natural language. The authors evaluate the model with respect to its compression ratio and information preservation, and measure information preservation in terms of the of the accuracy of a convolutional neural network in classifying the decompressed signal. The results show that the method can achieve up to 83% saving in storage. Also, the classification accuracy of the decompressed signals is around 92.41%. Furthermore, the method enables the user to balance the compression ratio and the required accuracy of the CNN classifiers.

Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques

Electrocardiogram (ECG) signals are the biomedical signals commonly used in the prognosis of cardiovascular diseases. ECG recordings need to be stored and transferred when telemedicine-based healthcare systems are required. These data are stored in a digitized format at higher bits per sample that requires ample space for storage. Therefore, this motivated us to develop efficient compression methodologies for ECG signals. In this regard, this work proposes compression techniques using the optimized tunable-Q wavelet transform (TQWT). For this purpose, TQWT parameters are optimized using several meta-heuristic optimization algorithms such as variants of PSO, ABC and its hybrid with PSO, GWO and its hybrid with PSO, and Sparse PSO. These hybrid methods and Sparse PSO have been utilized for the first time to optimize TQWT. Subsequently, thresholding and quantization are performed by using a dead-zone quantizer (DZQ). The quantized coefficients are encoded by utilizing a lossless compression technique run-length encoding (RLE). The proposed algorithms have been examined on the MIT-BIH arrhythmia database. It is clear from the results that significant compression has been achieved when compared to existing techniques. The performance of the proposed algorithms has been evaluated in terms of various evaluation parameters that are compression ratio (CR), percent-root-mean square difference (PRD), signal-to-noise ratio (SNR), and quality score (QS).

Efficient electrocardiogram signal compression algorithm using dual encoding technique

<span>In medical practices, the storage space of electrocardiogram (ECG) records is a major concern. These records can contain hours of recording, necessitating a large amount of storage space. This problem is commonly addressed by compressing the ECG signal. The proposed work deal with the ECG signal compression method for ECG signals using discrete wavelet transform (DWT). The DWT appeared as powerful tools to compact signals and shows a signal in another time-frequency representation. It is very appropriate in the elimination & removal of redundancy. The ECG signals are decomposed using DWT. After that, the coefficients that result from DWT are threshold depending on the energy packing efficiency (EPE) of the signal. The compression is achieved by the quantization and dual encoding techniques (run-length encoding & Huffman encoding). The dual encoding technique compresses data significantly. The result of the proposed method shows better performance with compression ratios and good quality reconstructed signals. For example, the compression ratio (CR)=20.6, 10.7 and 11.1 with percent root mean square difference (PRD)=1%, 0.9% and 1% for using different DWT (Haar, db2 and FK4) Respectively.</span>

A Novel Approach of ECG Signal Compression using MRA Analysis based on WPD

In this paper, an electrocardiogram (ECG) signal compression is proposed based on the wavelet packet transform (WPT) that explore the multi-resolution analysis (MRA). An ECG signal is primary tool to examine the cardiac health; sometime, its need to store a long duration data for review the health condition that consumed storage space. This challenge overcome through the suitable compression technique to save the storage space. Here, several experiments are carried out with proposed method and signals. The WPT generate the large number of sparse data of ECG signal that help to achieve the compression as it computes the 2n sub-bands as compare to (n+1) sub-band of discrete wavelet transform (DWT). The performance of the proposed method is evaluated in term of compression ratio/rate with other fidelity assessment. As results shows, 42% average compression achieved at cost of good and acceptable quality of reconstruction, it also compared with original signal in terms of correlation and waveform comparison.

MULTISCALE BSBL COMPRESSED SENSING-BASED ECG SIGNAL COMPRESSION WITH ENCODING FOR TELEMEDICINE

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

Impact of optimal scaling coefficients in bi-orthogonal wavelet filters on compressed sensing

PurposeIn today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.Design/methodology/approachA new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.FindingsFinally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.Originality/valueThis paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.

Quality Guaranteed ECG Signal Compression Using Tunable-Q Wavelet Transform and Möbius Transform-Based AFD

Long-term electrocardiogram (ECG) signal monitoring necessitates a large amount of memory space for storage, which affects the transmission channel efficiency during real-time data transfer. Using a combination of tunable-Q wavelet transform (TQWT) and adaptive Fourier decomposition (AFD), the proposed work develops a new single-channel ECG signal compression algorithm. The input parameters of TQWT were selected so that the lowest frequency subband contained highest energy along with minimal loss. A new Mobius transform-based AFD was introduced to improve the fidelity, by computing highest energy coefficients using Nevanlinna factorization, with suitable decomposition level. Finally, the lossless compression was performed in polar coordinate of final complex coefficients that significantly improved the compression ratio (CR). The algorithm was tested on “python” programming platform, tested in Raspberry Pi (R-Pi), for real-time data processing, and wireless transmission to cloud server and smartphone devices. The suggested work yielded CR, percent root mean square error (PRD), and PRD normalized (PRDN) of 30.06, 7.80, and 11.62, respectively, after testing on 48 Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ECG data with 30-min duration. A rigorous quality assessment of the reconstructed signal ensured that there was minimal impact on various characteristic domains in the ECG signal, enhancing its acceptability in medical applications.

A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform

With the advent of the technological era, the complexity of data security and privacy has gained utmost importance. People are becoming more conscious about the security and confidentiality of their crucial information. The present study is first of its kind effort in for safeguarding an individual’s electrocardiogram (ECG) data. ECG data not only does hold medical significance but also is of crucial and critical importance for purposes related to biometric information. Hence, a deciding factor for recognizing and validating personal information. The study progresses and proposes a new technique in ECG watermarking compression. This method ensures high reliability of data and information with confidentiality. The efficiency of the proposed research is evaluated in terms of compression ratio (CR), compressed signal-to-noise-ratio (CSNR), root-mean-square error (RMSE), maximum absolute error, percentage root-mean-square difference (PRD), and quality score. After a comprehensive evaluation, the final results show that the proposed work is robust and feasible.

A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM

Typical IoT based e-health scenarios use resource constrained wearable device to facilitate ubiquitous long-term monitoring for chronic conditions like cardiovascular disease (CVD). Electrocardiogram (ECG) is an efficient indicator to diagnose patients with CVD. In wearable technology, the signal transmission cost is high and the observed ECG signal is likely to be contaminated with noise. To amend this, an efficient lightweight signal compression scheme is designed to reduce the signal size before transmitting it and thereby reducing the transmission cost and allow long-term monitoring. In this paper, an edge based novel approach is proposed by combining convolutional denoising autoencoder (CDAE) and long short-term memory (LSTM) for ECG signal compression. A single layered LSTM network is added to the end of encoder section of the CDAE, instead of adding several convolutional filters and pooling layers. In which, the number of trainable parameters of the model are reduced and in turn lessen the computation time. Also, the LSTM network learns the order dependencies between the data that helps to reconstruct the data from its compressed form. In the meantime, the proposed algorithm denoises the signal as it employs Denoising Autoencoder architecture. The experiments are conducted on ECG signal taken from MIT-BIH Arrhythmias Database. The experimental result shows that the proposed method is efficient by achieving compression ratio of 64 with better reconstruction quality score of 15.61 which is higher than state-of-the-art methods. As well the proposed method is lightweight when compared with baseline methods CDAE and stacked autoencoder in terms of computation cost.

An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system

AbstractDue to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques

AbstractSignal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite where it is important that the original and the decompressed data should be identical or where deviations from the original data would lead to catastrophic consequences, especially in biomedical signal analysis and diagnostics. For which, the input signal preprocessed with differential pulse code modulation (DPCM) reduces the interchannel dependencies to get the desired output. A whole array of unique compression techniques are being utilized in the compression process. The combination of (K‐means clustering, arithmetic encoding [AE], Huffman encoding [HE]) clustering and coding compression techniques are analyzed using electro cardiogram (ECG) and electroencephalogram (EEG) signals. The proposed method employs k‐means clustering combined with Huffman encoding (DiKHE) and k‐means clustering combined with arithmetic encoding (DiKAE) individually. Compression ratio (CR) is analyzed with these combinations of compression techniques for various cluster size K (K = 2,3,4,5,6). A maximum CR of 6.03144 and 4.54126 is obtained for ECG and EEG signals respectively. The compressions based on these techniques are efficient since the compressed signal is reconstructed perfectly as it matches exactly with the original signal.

A Wi-Fi IoT prototype for ECG monitoring exploiting a novel Compressed Sensing method

<p><span lang="EN-US">The paper presents an Internet of Things (IoT) prototype which consists of a data acquisition device wirelessly connected to Internet via Wi-Fi, for continuous electrocardiogram (ECG) monitoring. The proposed system performs a novel Compressed Sensing (CS) based method on ECG signal with the aim of reducing the amount of transmitted data, thus realizing an efficient way to increase the battery life of such devices. For the assessment of the energy consumption of the device, an experimental setup was arranged and its description is presented. The evaluation of the reconstruction quality of the ECG signal in terms of Percentage of Root-mean-squared Difference (PRD</span><span lang="EN-US">) is reported for several Compression Ratios (CRs</span><span lang="EN-US">). The obtained experimental results clearly demonstrate the robustness and usefulness of the Wi-Fi based IoT devices adopting the considered CS-method for data compression of ECG signals. Furthermore, it allows reducing the energy consumption of the IoT device, by increasing the CR</span><span lang="EN-US">, without significantly degrading the quality of the reconstructed ECG signal.</span></p>