Percentage Root Mean Square Difference Research Articles

EMD AND WAVELET-BASED ECG SIGNAL DENOISING AND QRS COMPLEX DETECTION

This project presents a novel method for noise removal and QRS complex detection in electrocardiogram (ECG) signals, employing Empirical Mode Decomposition (EMD), windowing, and wavelet thresholding techniques. Noise is added to the ECG signal, and EMD is applied to obtain denoised components. Local minima detection and peak matching identify QRS complex boundaries. Windowing isolates the QRS regions, followed by wavelet thresholding for further denoising. Evaluation metrics like improved Signal-to-Noise Ratio, Mean Square Error, and Percent Root Mean Square Difference are calculated at different input SNR values, demonstrating the method's effectiveness in preserving the QRS complex while removing noise.. Keywords: ECG signal processing, QRS complex detection, Empirical Mode Decomposition (EMD), Windowing, Wavelet thresholding, QRS complex extraction, Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Percent Root Mean Square Difference (PRD).

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network.

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson's correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme.

Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.

An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform

During the recording time, electrocardiogram (ECG) signals are subject to multiple artefact noises, such as muscle activity, white Gaussian noise (WGN), baseline wander, and power line interference (PLI). Therefore, pre-processing of ECG signals is essential to eliminate these artefacts and to obtain efficient ECG features. Many approaches have been proposed for removing ECG noises, including ECG signal denoising using wavelet transform (WT). However, the effectiveness and performance of the WT technique are strongly related to the configuration of its control parameters, which are typically fine-tuned through a laborious and time-consuming series of experiments. This paper introduces a technique that combines particle swarm optimisation (PSO) with WT for ECG signal denoising. The key contribution of this research is the use of PSO to determine the optimum settings for all WT parameters for ECG signal denoising (type of wavelet basis function Φ, thresholding function β, level of decomposition L, rule for threshold selection λ, and rescaling method ρ). The efficiency of the proposed method is evaluated using the percentage root mean square difference (PRD) and the signal-to-noise ratio (SNR), employing various ECG signals available online from the MIT-BIH Arrhythmia database. Experimental results show that the proposed PSO-WT technique yields better results than state-of-the-art techniques in terms of SNR, particularly for PLI measured at 60 Hz and still acceptable at 50 Hz. For example, a denoised ECG signal resulting from the proposed technique at an SNR input of 10 dB corresponds to an SNRoutput of 27.47 dB at 60 Hz, improving the quality of the denoised ECG signal and making it more appropriate for clinical diagnosis. Furthermore, the proposed method also shows promising efficiency in the presence of WGN, making it highly relevant for IoT applications and RF transmission.

An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall

An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall

Discrete wavelet transform based processing of embroidered textile-electrode electromyography signal acquired with load and pressure effect

The diagnosis of neuromuscular diseases is complicated by overlapping symptoms from other conditions. Textile-based surface electromyography (sEMG) of skeletal muscles, offer promising potential in diagnosis, treatment, and rehabilitation of various neuromuscular disorders. However, it is important to consider the impact of load and pressure on EMG signals, as this can significantly affect the signal’s accuracy. This study seeks to investigate the influence of load and pressure on EMG signals and establish a processing framework for these signals in the diagnosis of neuromuscular diseases. The sEMG data were collected from healthy subjects using a textile electrode developed from polyester multi-filament conductive hybrid thread (CleverTex). The textrode was embroidered directly on an elastic bandage (Velcro® strap) placed on volunteer’s muscles while different activities were performed with varying loads and pressure. The collected data were pre-processed using standard techniques of the discrete wavelet transform to remove noise and artifacts. The performance of the proposed denoising algorithm was evaluated using the signal-to-noise ratio (SNR), percentage root mean square difference (PRD), and root mean square error (RMSE). Various signal processing approaches (filters) were considered and the results were compared with the proposed EMG noise reduction algorithms. Based on the experimental results, the fourth level of decomposition for the sym5 wavelets with the Rigrsure threshold method achieved the highest signal-to-noise ratio (SNR) values of 16.69 and 21.91, for soft and hard thresholding functions, respectively. The SNR values of 22.11, 21.54, and 2.78 at three different pressure levels 5 mmHg, 10 mmHg, and 20 mmHg, respectively, indicate the superior performance of wavelet multiresolution filter in de-noising applications. The results of this study suggest that our methodology is effective, precise, and reliable for analysing sEMG data and provide insights into both physiological and pathological neuromuscular conditions.

CT-based generation of synthetic-pseudo MR images with different weightings for human knee

Synthetic MR images are generated for their high soft-tissue contrast avoiding the discomfort by the long acquisition time and placing claustrophobic patients in the MR scanner's confined space. The aim of this study is to generate synthetic pseudo-MR images from a real CT image for the knee region in vivo. 19 healthy subjects were scanned for model training, while 13 other healthy subjects were imaged for testing. The approach used in this work is novel such that the registration was performed between the MR and CT images, and the femur bone, patella, and the surrounding soft tissue were segmented on the CT image. The tissue type was mapped to its corresponding mean and standard deviation values of the CT# of a window moving on each pixel in the reconstructed CT images, which enabled the remapping of the tissue to its MRI intrinsic parameters: T1, T2, and proton density (ρ). To generate the synthetic MR image of a knee slice, a classic spin-echo sequence was simulated using proper intrinsic and contrast parameters. Results showed that the synthetic MR images were comparable to the real images acquired with the same TE and TR values, and the average slope between them (for all knee segments) was 0.98, while the average percentage root mean square difference (PRD) was 25.7%. In conclusion, this study has shown the feasibility and validity of accurately generating synthetic MR images of the knee region in vivo with different weightings from a single real CT image.

Validation of Electrocardiogram Based Photoplethysmogram Generated Using U-Net Based Generative Adversarial Networks.

Photoplethysmogram (PPG) performs an important role in alarming atrial fibrillation (AF). While the importance of PPG is emphasized, there is insufficient amount of openly available atrial fibrillation PPG data. We propose a U-net-based generative adversarial network (GAN) which synthesize PPG from paired electrocardiogram (ECG). To measure the performance of the proposed GAN, we compared the generated PPG to reference PPG in terms of morphology similarity and also examined its influence on AF detection classifier performance. First, morphology was compared using two different metrics against the reference signal: percent root mean square difference (PRD) and Pearson correlation coefficient. The mean PRD and Pearson correlation coefficient were 27% and 0.94, respectively. Heart rate variability (HRV) of the reference AF ECG and the generated PPG were compared as well. The p-value of the paired t-test was 0.248, indicating that no significant difference was observed between the two HRV values. Second, to validate the generated AF PPG dataset, four different datasets were prepared combining the generated PPG and real AF PPG. Each dataset was used to optimize a classification model while maintaining the same architecture. A test dataset was prepared to test the performance of each optimized model. Subsequently, these datasets were used to test the hypothesis whether the generated data benefits the training of an AF classifier. Comparing the performance metrics of each optimized model, the training dataset consisting of generated and real AF PPG showed a test accuracy result of 0.962, which was close to that of the dataset consisting only of real AF PPG data at0.961. Furthermore, both models yielded the same F1 score of 0.969. Lastly,using only the generated AF PPG dataset resulted in test accuracy of 0.945, indicating that the trained model was capable of generating valuable AF PPG. Therefore, it can be concluded that the generated AF PPG can be used to augment insufficient data. To summarize, this study proposes a GAN-based method to generate atrial fibrillation PPG that can be used for training atrial fibrillation PPG classification models.

Experimental evaluation of ECG signal denoising methods based on HRV indices and their application in indoor thermal comfort study under different temperatures

The heart rate variability (HRV) features extracted from an electrocardiogram (ECG) signal have been widely used to help improve the performance of thermal comfort models, with the ECG signal denoising method being very important. In this study, 20 volunteers were recruited for experiments in a well-controlled chamber. Their ECG signals (a total of 6,000,000 sets of data) were collected under stable and variable temperature environments. With the use of the setup database, four denoising methods were compared: the fast Fourier transform (FFT), wavelet transform, Chebyshev filter, and Butterworth filter. When the performance of these methods was evaluated from ECG images, the Butterworth filter exhibited a more comprehensive and smoother representation of the original signal. Judging from denoising indices, the Butterworth filter exhibited the best performance, with its denoising indices surpassing those of fast Fourier transform and wavelet transform by differences ranging from 26% to 122%. Judging from HRV index and R-peak positioning, the data processed by Butterworth exhibited the highest reliability (93.5%), outperforming FFT (30.0%), the wavelet filter (72.5%), and the Chebyshev filter (78.5%). Furthermore, the 5th-order Butterworth filter exhibited the largest signal-to-noise ratio (SNR) with 1.1–34.4% larger and the smallest percent root-mean-square difference (PRD) with 15.2–66.1% smaller compared to other orders. Next, the characteristics of denoised HRV features under different temperatures were studied. During a temperature rise stage, the fitting R2 values of rMSSD, SDNN, and HR with temperature were 0.85, 0.64, and 0.78, respectively; meanwhile, during the subsequent temperature drop stage, the fitting R2 values were 0.78, 0.84, and 0.78, respectively. This suggests that changes in temperature can be effectively reflected by HRV indices. When gender was taken into consideration, the HR of females showed a 55.8% higher fitting level compared to males.

Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal

In the diagnosis and treatment of cardiac diseases, magnetocardiography (MCG) technology is characterized by non-invasive, non-contact, and high precision. Therefore, it has currently become a research hotspot in the field of new medical technologies. However, due to the weak signal of the magnetocardiography, the noise needs to be filtered after acquisition. In this paper, an optimized variational mode decomposition algorithm based on the improved threshold method and the improved whale optimization algorithm (WOA) is proposed to process the MCG signal. In order to improve the denoising accuracy, the improved whale optimization algorithm, VMD algorithm, and the improved threshold method are combined. Firstly, the correlation coefficients are obtained by the improved whale optimization algorithm to decompose the IMFs, then the baseline drift is removed by using the moving average method for the low-frequency IMFs, and then the improved thresholding algorithm is applied to each IMF. Finally, the denoised signal is obtained by integration. Experimental tests show that the algorithm has good denoising performance compared with similar algorithms and can filter environmental noise as much as possible without changing the original signal information. The proposed method has the highest Signal-to-Noise Ratio improvement (SNRimp) and Correlation Coefficient (CC) and the lowest Percentage Root Mean Square Difference (PDR). Also, the method is validated in real MCG signal processing. The proposed algorithm can be applied in the field of signal denoising, and it has a wide range of application backgrounds.

Power-line interference and baseline wander elimination in ECG using VMD and EWT

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD.

A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

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.

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding.

Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of-2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.

Spread-Spectrum Modulated Multi-Channel Biosignal Acquisition Using a Shared Analog CMOS Front-End.

The key challenges in designing a multi-channel biosignal acquisition system for an ambulatory or invasive medical application with a high channel count are reducing the power consumption, area consumption and the outgoing wire count. This paper proposes a spread-spectrum modulated biosignal acquisition system using a shared amplifier and an analog-to-digital converter (ADC). We propose a design method to optimize a recording system for a given application based on the required SNR performance, number of inputs, and area. The proposed method is tested and validated on real pre-recorded atrial electrograms and achieves an average percentage root- mean-square difference (PRD) performance of 2.65% and 3.02% for sinus rhythm (SR) and atrial fibrillation (AF), respectively by using pseudo-random binary-sequence (PRBS) codes with a code-length of 511, for 16 inputs. We implement a 4-input spread-spectrum analog front-end in a 0.18 μm CMOS process to demonstrate the proposed approach. The analog front-end consists of a shared amplifier, a 2nd order Σ∆ ADC sampled at 7.8 MHz, used for digitization, and an on-chip 7-bit PRBS generator. It achieves a number-of-inputs to outgoing-wire ratio of 4:1 while consuming 23 μA/input including biasing from a 1.8 V power supply and 0.067 mm2 in area.

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.

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

Recently, many highly accurate artificial intelligence (AI)-based prediction models have been developed for 12-lead electrocardiogram (ECG) signals. Therefore, if one can faithfully reconstruct 12-lead signals from a single-lead signal, such prediction models can seamlessly be integrated into various wearable devices that measure only a single-lead ECG signal. We aim to verify the performance of AI model that constructs estimated 12-lead ECG signals from a single-lead ECG signal. To train and validate AI models, we used 36,610 12-lead standard ECG (10 seconds) signals recorded from Ewha Womans University Medical Center between 23 May 2017 and 2 September 2020. In this study, we examined whether it is possible to generate ECGs that reflect the characteristics of various cardiovascular diseases such as LBBB, RBBB, and AF. All input signals were normalized by using a bandpass filter and into [-1, 1]. To reconstruct 12-lead signals from a single-lead signal, we developed a novel conditional generative adversarial network, called EKGAN, which is based on Pix2Pix [1] and consists of two generators and one discriminator. Then, the performance of EKGAN was evaluated with respect to root mean square error (RMSE), mean absolute error (MAE) and percentage root mean squared difference (PRD). We used 36,610 and 9,154 ECGs to train and test AI models that generate other leads from a Lead I signal. Table 1 shows the performance of EKGAN and Pix2Pix for generating all lead signals except Lead I, where EKGAN outperformed Pix2Pix in terms of all measures. Figure 1 shows an example of original and generated 12-lead ECG signals by EKGAN and Pix2Pix using only the Lead I signal. Twelve-lead ECG signals can faithfully be reconstructed from a single-lead ECG signal by using our GAN-based reconstruction model, which opens up a wide range of medical applications of wearable ECG devices.

An ECG denoising method based on adversarial denoising convolutional neural network

The Electrocardiogram (ECG) is widely used to diagnose heart disease. However, ECG recordings are often polluted by different noises in real situations. It is of great significance to propose an effective method for noise rejection of ECG signal. With the development of deep learning, neural networks have been widely utilized in signal processing. Most of the existing models of deep neural networks have millions of parameters, which places higher requirements on medical computing power. Based on this, this paper proposes a new denoising framework named the Adversarial Denoising Convolutional Neural Network (ADnCNN). We adopt the ADnCNN model to learn the residual signals in the noisy signal to obtain clean signal. A discriminator network is used for two-class classification of the denoised and clean signals, which are fed back to the ADnCNN model for parameter adjustment. The proposed method is validated by dividing three different types of datasets on the MIT-BIH arrhythmia database, the MIT-BIH noise stress test database, and the QT database for experiments. The denoising performance of the ADnCNN model is represented by the Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and Percentage Root Mean Square Difference (PRD). The generality and adaptability of the model are also demonstrated. Simulation results show that the proposed method has advantages compared to recently proposed models.

Compressed Sensing Approach for Physiological Signals: A Review

The immense progress in physiological signal acquisition and processing in health monitoring allowed a better understanding of patient disease detection and diagnosis. With the increase in data volume and power consumption, effective data compression, signal acquisition, transmission, and processing techniques are essential, especially in telemonitoring healthcare applications. An emerging research area focuses on integrating compressed sensing (CS) with physiological signals to deal with a massive amount of physiological data, transmission bandwidth, and power-saving purposes. A review of CS for physiological signals is presented in this article, including electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and electrodermal activity (EDA), focusing on the pros and cons of CS in treating such signals and the suitability of CS for hardware implementation. Furthermore, we emphasize performance matrices, such as compression ratio (CR), signal-to-noise ratio (SNR), Percentage Root-mean-square Difference (PRD), and processing time to evaluate the performance of CS. We also investigate the current practices, challenges, and opportunities of using CS in healthcare applications.

On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing

On-device computing in biomedical sensors has become attractive for developing wearable health monitoring systems. The challenge is to make a compromise between the latency and complexity in a resource-constrained implementation. This article describes an on-device implementation of multi-level signal quality aware and quality controlled compression (MSQQCC) that enhances the compression factor while preserving the clinical features in a wearable photoplethysmography (PPG) sensing application. The multi-level quality assessment (QA) provides three eligible PPG qualities, viz., “excellent,” “good,” and “average,” based on which corresponding upper limits are set for further compression using a discrete wavelet transform, while the “corrupted” segments are discarded. A pretrained multilayer perceptron neural network (MLPNN) provides the optimal quantization level of coefficients. The residual data is separately compressed using an autoencoder (AE). MSQQCC was evaluated with 300 min of PPG data from three public data sets and 110 min of data collected at a laboratory. The end-to-end pipeline was implemented in a standalone system with an ARM Cortex A53 controller, requiring 35.51 kB of memory and 1.8 s latency to process 4 s PPG data. The on-device QA achieved 98.45% overall accuracy (Ac), which outperforms published works on PPG QA. The mean deviation of PPG clinical features by 5%, with overall compression ratio (CR) and percentage root mean squared difference (PRD) were 40.85 and 2.52, which are superior to many published works. Real-time transmission over Bluetooth shows an improvement of energy efficiency by a significant factor and a 34% extended battery life for wearable PPG sensors. The results are encouraging for the adoption of MSQQCC in wearable biomedical health monitoring.