Blind Restoration of Real-World Audio by 1D Operational GANs

Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1

Blind ECG Restoration by Operational Cycle-GANs

Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.publishedVersionPeer reviewe

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performances. This study proposes a novel approach to narrow this gap and propose a real-time solution with shallow and compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)

Comparative evaluation of the applicability of self-organized operational neural networks to remote photoplethysmography

Abstract. Photoplethysmography (PPG) is a widely applied means of obtaining blood volume pulse (BVP) information from subjects which can be used for monitoring numerous physiological signs such as heart rate and respiration. Following observations that blood volume information can also be retrieved from videos recorded of the human face, several approaches for the remote extraction of PPG signals have been proposed in literature. These methods are collectively referred to as remote photoplethysmography (rPPG). The current state of the art of rPPG approaches is represented by deep convolutional neural network (CNN) models, which have been successfully applied in a wide range of computer vision tasks. A novel technology called operational neural networks (ONNs) has recently been proposed in literature as an extension of convolutional neural networks. ONNs attempt to overcome the limitations of conventional CNN models which are primarily caused by exclusively employing the linear neuron model. In addition, to address certain drawbacks of ONNs, a technology called self- organized operational neural networks (Self-ONNs) have recently been proposed as an extension of ONNs. This thesis presents a novel method for rPPG extraction based on self-organized operational neural networks. To comprehensively evaluate the applicability of Self-ONNs as an approach for rPPG extraction, three Self-ONN models with varying number of layers are implemented and evaluated on test data from three data sets representing different distributions. The performance of the proposed models are compared against corresponding CNN architectures as well as a typical unsupervised rPPG pipeline. The performance of the methods is evaluated based on heart rate estimations calculated from the extracted rPPG signals. In the presented experimental setup, Self-ONN models did not result in improved heart rate estimation performance over parameter-equivalent CNN alternatives. However, every Self-ONN model showed superior ability to fit the train target, which both shows promise for the applicability of Self-ONNs as well as suggests inherent problems in the training setup. Additionally, when taking into account the required computational resources in addition to raw HR estimation performance, certain Self-ONN models showcased improved efficiency over CNN alternatives. As such, the experiments nonetheless present a promising proof of concept which can serve as grounds for future research.Vertaileva arviointi itseorganisoituvien operationaalisten neuroverkkojen soveltuvuudesta etäfotopletysmografiaan. Tiivistelmä. Fotopletysmografia on laajasti sovellettu menetelmä veritilavuuspulssi-informaation saamiseksi kohteista, jota voidaan käyttää useiden fysiologisten arvojen, kuten sydämensykkeen ja hengityksen, seurannassa. Seuraten havainnoista, että veritilavuusinformaatiota on mahdollista palauttaa myös ihmiskasvoista kuvatuista videoista, useita menetelmiä fotopletysmografiasignaalien erottamiseksi etänä on esitetty kirjallisuudessa. Yhteisnimitys näille menetelmille on etäfotopletysmografia (remote photoplethysmography, rPPG). Syvät konvolutionaaliset neuroverkkomallit (convolutional neural networks, CNNs), joita on onnistuneesti sovellettu laajaan valikoimaan tietokonenäön tehtäviä, edustavat nykyistä rPPG-lähestymistapojen huippua. Uusi teknologia nimeltään operationaaliset neuroverkot (operational neural networks, ONNs) on hiljattain esitetty kirjallisuudessa konvolutionaalisten neuroverkkojen laajennukseksi. ONN:t pyrkivät eroon tavanomaisten CNN-mallien rajoitteista, jotka johtuvat pääasiassa lineaarisen neuronimallin yksinomaisesta käytöstä. Lisäksi tietyistä ONN-mallien heikkouksista eroon pääsemiseksi, teknologia nimeltään itseorganisoituvat operationaaliset neuroverkot (self-organized operational neural networks, Self-ONNs) on hiljattain esitetty lajeennuksena ONN:ille. Tämä tutkielma esittelee uudenlaisen menetelmän rPPG-erotukselle pohjautuen itseorganisoituviin operationaalisiin neuroverkkoihin. Self-ONN:ien soveltuvuuden rPPG-erotukseen perusteelliseksi arvioimiseksi kolme Self-ONN -mallia vaihtelevalla määrällä kerroksia toteutetaan ja arvioidaan testidatalla kolmesta eri datajoukosta, jotka edustavat eri jakaumia. Esitettyjen mallien suorituskykyä verrataan vastaaviin CNN-arkkitehtuureihin sekä tyypilliseen ohjaamattomaan rPPG-liukuhihnaan. Menetelmien suorituskykyä arvioidaan perustuen rPPG-signaaleista laskettuihin sydämensykearvioihin. Esitellyssä kokeellisessa asetelmassa Self-ONN:t eivät johtaneet parempiin sykearvioihin verrattuna parametrivastaaviin CNN-vaihtoehtoihin. Self-ONN:t kuitenkin osoittivat ylivertaista kykyä sovittaa opetuskohteen, mikä sekä on lupaavaa Self-ONN:ien soveltuvuuden kannalta että viittaa luontaisiin ongelmiin opetusasetelmassa. Lisäksi, kun huomioon otetaan vaaditut laskentaresurssit raa’an sykkeen arvioinnin suorituskyvyn lisäksi, tietyt Self-ONN -mallit osoittivat parempaa tehokkuutta CNN-vaihtoehtoihin verrattuna. Näin ollen kokeet joka tapauksessa tarjoavat lupaavan konseptitodistuksen, joka voi toimia perustana tulevalle tutkimukselle

Zero-Shot Motor Health Monitoring by Blind Domain Transition

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.Comment: 13 pages, 9 figures, Journa

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance.  Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance:  As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity. Conclusion: This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used.publishedVersionPeer reviewe

Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks

Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported.publishedVersionPeer reviewe