Signal Quality Assessment Research Articles

Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module.

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. High-quality PPG signals are necessary to extract cardiores-piratory information accurately. Motion artifacts can easily corrupt PPG signals due to human locomotion, leading to noise enriched, poor quality signals. Several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation are available, but those algorithms' efficacy is questionable. So, the authors propose a lightweight CNN architecture for signal quality assessment by employing a novel Quantum Pattern Recognition (QPR) technique. The proposed algorithm is validated on manually annotated data obtained from the University of Queensland database. A total of 28366, 5s signal segments are preprocessed and transformed into image files of 20 x 500 pixels for input to the 2D CNN architecture. The developed model classifies the PPG signal as 'good' and 'bad' with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. The experimental analysis concludes that slim module based architecture and novel Spatio-temporal pattern recognition technique improve the system's performance. The proposed approach is suitable for a resource-constrained wearable implementation.

RF-Free infant ECG monitoring: Performance and signal quality assessment.

Existing Electrocardiogram (ECG) systems are either wired or based on radiofrequency (RF) wireless devices when remote transmission is needed. However, the use of radiofrequencies has limitations especially for sensitive populations such as newborns and infants. In addition, due to electromagnetic interference problems, impairments in the RF transmission of the ECG signal can lead to diagnostic errors. To answer these problems, we propose in this article, an optical wireless monitoring system, using an infrared link between an ECG sensor placed on a baby's chest and receivers placed on the ceiling of a pediatric room. In addition, it is assumed that the infant can move around in his bed. Our main contribution is the evaluation of the quality of the ECG signal transmitted by the proposed system, in terms of classic Signal to Noise Ratio (SNR) and Bit Error Rate (BER) metrics but also in terms of Signal Quality Indexes (SQIs) calculated from the characteristics of the received ECG signal. Results show the ability to perform wireless infant ECG monitoring using the Optical Wireless Communication (OWC) with satisfactory quality for optimum power.

Morphological Event Based Signal Quality Assessment of Electrocardiogram.

ECG signals acquired from mobile devices by unskilled users are corrupted with several noises. Poor signal quality may result in an increased number of false alarms, degrading diagnostic performance, and increasing the burden on the doctors in decoding the information for further clinical intervention. So, it is necessary to assess the quality of the signals before doing any further processing. This paper presents a method for accessing the reliability of ECG signals obtained from wearable sensors. A morphological event-based quality assessment method is proposed where a signal will be classified as GOOD/BAD. Results show that our method can achieve an accuracy = 92 % with sensitivity = 0.98 and specificity = 0.59.

A review of arrhythmia detection based on electrocardiogram with artificial intelligence

Introduction With the widespread availability of portable electrocardiogram (ECG) devices, there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia mainly relies on the rules of medical knowledge, which are insufficient due to the limitations of data quality and human expert knowledge. The research of arrhythmia detection methods based on artificial intelligence (AI) techniques can assist physicians in high-precision arrhythmia diagnosis. AI algorithms can also be embedded in smart ECG devices to help more people perform early screening for arrhythmia. Areas covered The primary objective of this paper is to describe the application of AI methods in the process of arrhythmia detection. Meanwhile, the advantages and limitations of various approaches in different applications are summarized to provide guidance and reference for future research work. Expert opinion Machine learning (ML) and deep learning (DL) algorithms can be more effectively employed to handle ECG signal denoising and quality assessment, wave detection and delineation, and arrhythmia classification problems. The DL approach can automatically learn deep representation features and temporal features of the ECG signal for heartbeat or rhythm classification. The application of AI methods for arrhythmia detection systems will significantly relieve the pressure on physicians to analyze ECGs.

Fully Automated Transcranial Doppler Ultrasound for Middle Cerebral Artery Insonation

Background: Transcranial Doppler ultrasound (TCD) is utilized in the assessment of neurological conditions in clinical environments such as the intensive care unit and emergency department. However, obstacles for widespread use of TCD include a lack of trained registered vascular technologists (RVT) and operator variability. We present a study comparing RVT and a fully automated robotic TCD system (NovaGuide rTCD) for insonation of the middle cerebral artery (MCA).Methods: A trained RVT and rTCD sequentially collected bilateral MCA cerebral blood flow velocity (CBFV) from 86 healthy subjects. Mean CBFV (mCBFV) and the signal quality assessment (SQA) acquired manually by RVT and autonomously via rTCD were compared. Comparison metrics evaluated include mean accuracy ratio (MAR), and Bland-Altman mean-difference (MD) between rTCD and RVT with paired t-Test for significance. Bootstrapping was used in the accuracy ratio and mean-time to best signal computations to establish 95% confidence intervals.Results: The mCBFVs and SQAs found by rTCD compared to RVT had MAR of 99.7% (97.7-101.7%) and 102.7% (101.1-104.8%), respectively. The rTCD mean-time to best-quality signal was 0.87 min (0.71-1.05) (RVT was not timed). The mean-difference scores for mCBFV and SQA were MD=-0.43cm/s (<i>p</i>=0.053) and MD=-0.36 (<i>p</i>=0.61), respectively. The rTCD had a 3.5% no-window failure rate compared to RVT no-window rate of 4.1%.Conclusion: Comparison of bilateral TCD signals collected by rTCD and RVT demonstrated equivalence in mCBFV and signal quality, suggesting rTCD’s potential to expand utility of TCD in clinical settings that are resource-limited.

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening ( of data). The model achieves an average 10-fold cross-validated AUC of . The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.

Representativeness consideration in the selection of classification algorithms for the ECG signal quality assessment

Background and Objectives: With the progress of digitalization, electrocardiograms (ECGs) are increasingly measured by embedded and portable devices, which may lead to significant degradation of signal quality due to noise and artifacts. This leads to the necessity of signal quality assessment before ECG interpretation. Especially, if the ECG training database is not balanced between bad and good signals the classification accuracy and the data distribution on the two classes are affected. In this paper, a comparative study is elaborated between 10 re-sampling techniques for data balancing, which have been applied with the random forest classifier. Methods: Based on this study, we propose a novel metric to consider the representativeness of the original data, to guaranty the closeness of the quality assessment results to the initial classes’ partition. The evaluation of the classifier’s performance is based on two criteria, which are the classifier training performance and the representativeness coefficient. It is to note that this novel measure is a complementary metric for classification evaluation and the classifier performance has the first priority. We refer thereby to a multi-objective optimization (MOO) method as a tool to reconcile both criteria. Results: The hybrid balancing technique SMOTETomek fulfills both criteria in a good manner. It reaches a high training performance and maintains the data representativeness. Conclusion: It is not necessary for a classifier algorithm with the best classification performance to be able to maintain the representativeness of the data, which affirms the importance of this study. Thus the proposed new metric should be taken into consideration for the selection of the classification method when dealing with new resulted data after discarding the bad qualified signals.

GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III

The nonlinear navigation payload transmission channel introduces errors at a system level, resulting in a distortion of the received signal and affecting differential positioning users. This work models the transmission channel of the navigation payload and uses the joint estimation algorithm to calculate the digital distortion and analog channel response of the transmission channel from the received signal. On the above basis, the algorithm and model are verified by GPS-L5 measured data, and the channel characteristics of GPS II and GPS III are deeply analyzed. The results show that the existing GPS IIF satellites have a digital distortion, which is negligible in GPS III satellites. Regarding the analog channel response, GPS III has better amplitude and group delay consistency than GPS II. Lastly, the ranging bias caused by the transmission channel of the navigation payload is provided to the user for reference, and GPS III is improved by approximately 0.02 m compared with GPS II. This study provides support for adjusting the satellite signal pre-distortion and enriches subsequent GNSS signal quality assessment work; it can also be used as a reference for differential positioning users.

Deep convolutional neural network-based signal quality assessment for photoplethysmogram

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was to develop a signal quality assessment technology for photoplethysmogram (PPG) widely used in wearable healthcare. In this study, we developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database. The DNN model was implemented through a 1D convolutional neural network (CNN). The number of CNN layers, number of fully connected nodes, dropout rate, batch size, and learning rate of the model were optimized through Bayesian optimization. As a result, 6 CNN layers, 1,546 fully connected layer nodes, 825 batch size, 0.2 dropout rate, and 0.002 learning rate were needed for an optimal model. Performance metrics of the result of classifying waveform quality into ‘Good’ and ‘Bad’, the accuracy, specificity, sensitivity, area under the receiver operating curve, and area under the precision–recall curve were 0.978, 0.948, 0.993, 0.985, 0.980, and 0.969, respectively. Additionally, in the case of simulated real-time application, it was confirmed that the proposed signal quality score tracked the decrease in pulse quality well.

LPWAN Coverage Assessment Planning Without Explicit Knowledge of Base Station Locations

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, narrowband IoT (NB-IoT), Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, the Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. In contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as the natural neighbor, linear interpolation, or inverse-distance weighting, can achieve comparable (and occasionally even better) accuracy levels.

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine–SVM, and convolutional neural network–CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Abstract Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

Automated Biomedical Signal Quality Assessment of Electromyograms: Current Challenges and Future Prospects

This work is an exposition of research supporting efforts to automate quality assessment in surface electromyography (sEMG). Electromyography measures electrical activity from skeletal muscles, which are the muscles associated with voluntary movements. Skeletal muscles can consist of tens to hundreds of thousands of contractile fibers which are grouped into functional units called Motor Units (MUs). Each MU is driven to contract its fibers by electrochemical impulses sent from a motor neuron, and a muscle may contain anywhere from tens to hundreds of MUs. When an MU is activated, all fibers associated with it are simultaneously activated. When fibers are activated, an electrochemical impulse propagates along each of them to cause contraction. This impulse is known as a Single Fiber Action Potential (SFAP). A Motor Unit Action Potential (MUAP) is the summation of all SFAPs corresponding to an activated MU. To generate a continuous contraction, MUs are repeatedly activated, producing a train of MUAPs, and this pulse train is called the Motor Unit Action Potential Train (MUAPT). The combined electrical activity of all of the MUAPTs associated with a contraction, as recorded on the surface of the skin using non-invasive electrodes, is a surface electromyography (sEMG) signal. In general, it is not possible to directly observe the SFAPs, MUAPs, and MUAPTs non-invasively (though there have been algorithmic efforts to decompose sEMG signals into its constituent MUAPTs). SFAPs and MUAPTs can be observed by inserting needle electrodes into specific muscle regions for high spatial selectivity; however, such techniques are invasive, making surface measurements a desirable alternative. sEMG signals have been used in a wide variety of applications, including fatigue assessment, myoelectric control, diagnosis of neuromuscular disorders, and tracking performance in sports.

Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals.

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

Reducing the Impact of External Vibrations on Fiducial Point Detection in Seismocardiogram Signals.

Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET-a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings.

Remote measurement of heart rate from facial video in different scenarios

Vital signs, such as heart rate (HR), can be remotely measured from facial video recorded by a consumer-level digital camera. However, the accuracy of heart rate estimation is typically affected by ambient light and body motions. Hence, we propose an anti-disturbance method that integrates Eulerian video magnification (EVM), signal quality assessment (QA), and adaptive chirp model decomposition (ACMD) to measure HR in the distance. Next, we evaluate and validate the performance of the proposed method in five different scenarios, including low-illumination, normal-illumination, high-illumination, unbalanced-illumination, and head-motion. The experimental results demonstrated that the estimations of HR using the proposed method in different scenarios have a high consistency with the corresponding ground truths. Moreover, comparing with the methods based on empirical mode decomposition (EMD) or variable mode decomposition (VMD) algorithms, the proposed method can provide a robust solution to eliminate disturbance and obtain a precise HR measurement from the facial videos in different scenarios.

Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches.

Photoplethysmography (PPG) is an important signal which contains much physiological information like heart rate and cardiovascular health etc. However, PPG signals are easily corrupted by motion artifacts and body movements during their recordings, which may lead to poor quality. In order to accurately extract cardiovascular information, it is necessary to ensure high PPG quality in these applications. Although there are several existed methods to get the PPG signal quality, those algorithms are complex and the accuracies are not very high. Thus, this work proposes a deep learning network for the signal quality assessment using the STFT time-frequency spectra. A total of 5804 10s signals are preprocessed and transformed into 2D STFT spectra with 250 × 334 pixels. The STFT figures are as the input of the CNN networks, and the model gives the result as good or bad quality. The model accuracy is 98.3% with 98.9% sensitivity, 96.7% specificity, and 98.8% F1-score. And the heart rate error is much reduced after classification with the reference of ECG signals. Thus, the proposed deep learning approaches can be useful in the classification of good and bad PPG signals. As far as we know, this is the first article using deep learning methods combined with STFT time-frequency spectra to get the signal quality assessment of PPG signals.

Transfer learning of CNN-based signal quality assessment from clinical to non-clinical PPG signals.

Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variation inside the micro-circulation. PPG technology is widely used in a variety of clinical and non-clinical devices in order to investigate the cardiovascular system. One example of clinical PPG device is the pulse oxymeter, while non-clinical PPG devices include smartphones and smartwatches. Such a wide diffusion of PPG devices generates plenty of different PPG signals that differ from each other. In fact, intrinsic device characteristics strongly influence PPG waveform. In this paper we investigate transfer learning approaches on a Covolutional Neural Network based quality assessment method in order to generalize our model across different PPG devices. Our results show that our model is able to classify accurately signal quality over different PPG datasets while requiring a small amount of data for fine-tuning.Clinical relevance- A precise detection and extraction of high quality PPG segments could enhance significantly the reliability of the medical analysis based on the signal.

Transcutaneous Cervical Vagus Nerve Stimulation Inhibits the Reciprocal of the Pulse Transit Time's Responses to Traumatic Stress in Posttraumatic Stress Disorder.

Research has shown that transcutaneous cervical vagus nerve stimulation (tcVNS) yields downstream changes in peripheral physiology in individuals afflicted with posttraumatic stress disorder (PTSD). While the cardiovascular effects of tcVNS have been studied broadly in prior work, the specific effects of tcVNS on the reciprocal of the pulse transit time (1/PTT) remain unknown. By quantifying detectable effects, tcVNS can be further evaluated as a counterbalance to sympathetic hyperactivity during distress - specifically, we hypothesized that tcVNS would inhibit 1/PTT responses to traumatic stress. To investigate this, the electrocardiogram (ECG), photoplethysmogram (PPG), and seismocardiogram (SCG), were simultaneously measured from 24 human subjects suffering from PTSD. Implementing state-of-the-art signal quality assessment algorithms, relative changes in the pulse arrival time (PAT) and the pre-ejection period (PEP) were estimated solely from signal segments of sufficient quality. Thereby computing relative changes in 1/PTT, we find that tcVNS results in reduced 1/PTT responses to traumatic stress and the first minute of stimulation, compared to a sham control (corrected p < 0.05). This suggests that tcVNS induces inhibitory effects on blood pressure (BP) and/or vasoconstriction, given the established relationship between 1/PTT and these parameters.Clinical Relevance- Relative changes in 1/PTT are induced by varying vasomotor tone and/or BP - it has therefore piqued considerable interest as a potential surrogate of continuous BP. Studying its responses to tcVNS thus furthers understanding of tcVNS-induced cardiovascular modulation. The positive effects detailed herein suggest a potential role for tcVNS in the long-term management of PTSD.

Real-time multi-class signal quality assessment of photoplethysmography using machine learning technique

Photoplethysmography (PPG) signal quality assessment (SQA) ensures improved measurements of various surrogate cardiovascular measurements like heart rate, SpO2, blood pressure, cardiac output and many more and as well reduces false alrams in ambulatory measurements. Although PPG SQA (PSQA) is a well researched area, but multiclass prediction of signal quality and its hardware implementation is limited. In this paper, a new non-segmenting approach for multiclass PSQA is presented with an optimal set of seven time-frequency domain features in conjuction with supervised classifiers to identify clean, partly clean and corrupted PPG data. The present work was carried out in three major stages- signal (dataset) acquisition, feature extraction and 3-class classification using random forest classifier. The technique was evaluated with volunteers’ data, MIMIC-III data from PhysioNet, CSL data, and combined data and achieved an overall average accuracy of 96.8% with good (0.98 or above) sensitivity, specificity, F1 score, precision and recall. On-device implementation of the proposed PSQA was accomplished using 7.2 h of PPG dataset on a quad-core Broadcom BCM 2837 controller with 1.2 GHz, supported by 1 GB RAM-based standalone device, with an accuracy of 97.09%. The latency and the peak memory requirement of the implementation was 0.81 s and 19 kB respectively to process 3 s PPG data. The present technique may be integrated as a part of a remote healthcare system using wearable sensors.