In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

https://www.mdpi.com/2076-3417/9/11/2331/pdf

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Neural Network for Automatic Classification of Pathological Voice Signals.

The utilization of upper extremity exoskeleton robots has been proved to be a scientifically effective approach for rehabilitation training. In the process of rehabilitation training, it is necessa...

https://journals.sagepub.com/doi/pdf/10.1177/1729881420974295

A novel fatigue detection method for rehabilitation training of upper limb exoskeleton robot using multi-information fusion:

There are several parameters that can be modulated during electrical stimulation-induced muscle contraction to obtain external work, i.e., Functional Electrical Stimulation (FES). The literature has several reports of the relationships of parameters such as frequency, pulse width, amplitude and physiological or biomechanical outcomes (i.e., torque) when these parameters are changed. While these relationships are well-described, lesser known across the literature is how changing the duty cycle (time ON and time OFF) of stimulation affects the outcomes. This review provides an analysis of the literature pertaining to the duty cycle in electrical stimulation experiments. There are two distinct sections of this review – an introduction to the duty cycle and definitions from literature (part I); and contentions from the literature and proposed frameworks upon which duty cycle can be interpreted (part II). It is envisaged that the two reviews will highlight the importance of modulating the duty cycle in terms of muscle fatigue in mimicking physiological activities. The frameworks provided will ideally assist in unifying how researchers consider the duty cycle in electrical stimulation (ES) of muscles.

/pdf/reporting-for-duty-the-duty-cycle-in-functional-electrical-13nmskgk1b.pdf

Reporting for Duty: The duty cycle in Functional Electrical Stimulation research. Part I: Critical commentaries of the literature.

This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.

/pdf/assessment-of-muscle-activity-using-electrical-stimulation-4uemcoiko4.pdf

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity.

/pdf/mechanomyography-based-muscle-fatigue-detection-during-4280k4o3ks.pdf

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

The idea of an FES is to stimulate the contraction of paralyzed muscles by inducing electrical pulses. Recent studies have demonstrated that the intervention of Functional Electrical Stimulation (FES) have improved patients with paralyzed muscle injuries. Unfortunately, due to the high cost of an FES device, rehabilitation centers in local hospitals are not equipped with FES devices. Generally, an FES device consists of electrodes and a stimulator. The stimulator of an FES device will act as the main controller that will provide with the activation or stimulation functions. This paper investigates the parameters that need to be addressed in designing an FES stimulator. While there are many different types of electrodes to be used in FES system, for this early work, we only look at FES systems application using only skin surface electrodes (non-invasive).

The design of non-invasive functional electrical stimulation (FES) for restoration of muscle function

Functional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

/pdf/electrical-stimulator-with-mechanomyography-based-real-time-40f28bj79u.pdf

Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study.

Functional Electrical Stimulation (FES) is a method of artificially stimulating muscles or nerves in order to result in contraction or relaxation of muscles. Many studies have shown that FES system has helped patients to live a better lives especially those who are suffering from physical mobility. Unfortunately, one of the main limitations of an FES system besides of its high cost is largely due to muscle fatigue. Muscle fatigue will affect the training duration which could delay patients' recovery rate. In this paper, we analyzed the occurrence of this fatigue phenomenon in terms of stimulator parameters such as amplitude, frequency, pulse width and pulse shape. The objective of this investigation is to identify other key features of the FES system parameters in order to prolong the training duration among patients. The experiment has been done on a healthy person for the duration of one minute and later the muscles response will be observed. Resultant muscle response is recorded as force using force resistive sensor. The experimental results show muscles will get fatigue at a different rate as the frequency increases. The experiment also shows that the duty cycle is reciprocal to the resultant force.

https://iopscience.iop.org/article/10.1088/1757-899X/53/1/012067/pdf

An investigation of fatigue phenomenon in the upper limb muscle due to short duration pulses in an FES system

While electrical stimulation has proven to produce positive outcome among patients, electrical stimulation in post stroke rehabilitation faced one main limitation – muscle fatigue. Muscle fatigue limits the training time, hence, affecting the recovery process. This work analyzes the occurrence of muscle fatigue with respect to the different stimulator parameters; amplitude, pulse shape and frequency. The detection of muscle fatigue will be monitored as force where the force sensitive resistor (FSR) sensors are used to detect the variations of the force exerted by the muscle. In this work, it is assumed that the fatigue in muscle will happen when the force recorded reduces to 60% from its initial force over the stimulation period. The experiment results proof that the stimulator parameters, amplitude, pulse shape and frequency, have different effect to the upper limb muscle with respect to muscle fatigue. The results also show that the exponential waveform can be considered in future rehabilitation program since the onset of muscle fatigue is delayed later in the experiment when compared to the other signals. This allows extended training duration among stroke patients to benefit from the recovery window time frame especially for patients who are still in their early recovering stage.

Jannatul Naeem

Papers

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

The design of non-invasive functional electrical stimulation (FES) for restoration of muscle function

Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study.

An investigation of fatigue phenomenon in the upper limb muscle due to short duration pulses in an FES system

Analysis on the effect of stimulator parameters in electrical stimulation procedure on the human bicep muscle