Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note.

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.

Deep learning in multimedia healthcare applications: a review.

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens' health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area.

Using Cognitive-Motor Dual-Tasks and Functional Near-Infrared Spectroscopy to Characterize Older Adults with and without Subjective Cognitive Decline.

BACKGROUND: Subjective cognitive decline (SCD) refers to individuals who report persistent cognitive deficits but perform normally on neuropsychological tests. Performance may be facilitated by increased prefrontal cortex activation, known as neural compensation, and could be used to differentiate between older adults with and without SCD. OBJECTIVE: This cross-sectional pilot study measured changes in the hemodynamic response (ΔHbO2) using functional near-infrared spectroscopy (fNIRS) as well as cognitive and motor performance during fine and gross motor dual-tasks in older adults with and without SCD. METHODS: Twenty older adults over 60 years old with (n = 10) and without (n = 10) SCD were recruited. Two experiments were conducted using 1) gross motor walking and 2) fine motor finger tapping tasks that were paired with an n-back working memory task. Participants also completed neuropsychological assessments and questionnaires on everyday functioning. RESULTS: Repeated measures ANOVAs demonstrated slower response times during dual-task gait compared to the single task (p = 0.032) and in the non-SCD group, slower gait speed was also observed in the dual compared to single task (p = 0.044). Response times during dual-task finger tapping were slower than the single task (p = 0.049) and greater ΔHbO2 was observed overall in the SCD compared to non-SCD group (p = 0.002). CONCLUSIONS: Examining neural and performance outcomes revealed differences between SCD and non-SCD groups and single and dual-tasks. Greater brain activation during dual-task finger tapping may reflect neural compensation, which should be examined in a larger sample and longitudinally to better characterize SCD.

Adaptive Spectro-Temporal Filtering for Electrocardiogram Signal Enhancement.

Advances in low-cost portable electrocardiogram (ECG) devices have opened doors for numerous new applications, including fitness tracking, remote health, and peak athletic performance monitoring, to name a few. Many such devices, however, have been shown to be highly contaminated by movement and/or muscle contraction artifacts, which, in turn, can lead to erroneous heart rate and heart rate variability (HRV) analyses. Here, we propose a new denoising method based on adaptive spectro-temporal filtering for ECG enhancement. The algorithm relies on the so-called modulation spectral signal representation, which is shown to accurately separate ECG and noise components. The proposed method was tested on synthetic ECG signals corrupted with varying levels of recorded noise and on long-term bedside noisy ECG recordings. Gains over a state-of-the-art wavelet-based denoising algorithm were achieved, particularly for very noisy scenarios. Overall, the proposed algorithm achieved a 61.8% gain in signal-to-noise ratio improvement, a three times reduction in average heart rate measurement error, and a 15% reduction in HRV measurement error relative to the benchmark, thus suggesting that it is an ideal candidate for ECG-based fitness/athletic monitoring applications.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement.

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).