Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities - audio, visual, and physiological - to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90 . These results lay the foundation for applications of affective computing in real-world domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.
Physiological markers of autonomic nervous activity include galvanic skin response (GSR), electroencephalogram (EEG), electromyogram (EMG), respiration, skin temperature (ST), electrocardiogram (ECG) and photoplethysmogram (PPG) @cite_4 . Note that a heartbeat time series can easily be extracted from both ECG and PPG in the form of inter-beat-intervals (IBIs). Existing approaches for ED P typically pool a number of biosignals as multimodal input to classifier algorithms @cite_11 @cite_0 @cite_35 . However, this directly contrasts the near-unimodal nature of affordable wearable devices in use today. Comparatively few studies narrow their scope in accordance with these practical limitations.
@cite_0 @cite_35 @cite_4 @cite_11
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This paper considers signals from the cardiovascular system for possible use in biometric authentication. The signals of particular interest here derive from photoplethysmography (PPG), which refers to the use of illumination-based sensors that are sensitive to volumetric changes as blood travels through the body. Photoplethysmography sensors have been developed for the fingertip and the ear lobe, and they provide a convenient, noninvasive means of measuring heart rate and heart-rate variability. We demonstrate in this paper that PPG-based signals also have the potential to be used for biometric authentication, even though PPG signals appear to convey much less information than their electromagnetic counterparts, electrocardiograms (ECG). Through a novel decomposition into a sum-of-Gaussians representation, we present experimental results that indicate rank-1 accuracies of 90 and 95 with 2 seconds and 8 seconds of PPG test signal data, respectively. To our knowledge, this paper is the first to demonstrate robust PPG-based authentication for subjects with different emotional states. We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine. Abstract Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG . The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63 ) and of supraventricular ectopic beats (95.57 ) at a low sampling rate of 114 Hz . Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fet al ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.