scholarly journals Deep learning for healthcare applications based on physiological signals: A review

2018 ◽  
Vol 161 ◽  
pp. 1-13 ◽  
Author(s):  
Oliver Faust ◽  
Yuki Hagiwara ◽  
Tan Jen Hong ◽  
Oh Shu Lih ◽  
U Rajendra Acharya
Keyword(s):  
Deep Learning ◽  
Physiological Signals ◽  
Healthcare Applications

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications

2020 ◽  
Vol 79 (41-42) ◽  
pp. 31663-31690
Author(s):  
Debadyuti Mukherjee ◽  
Riktim Mondal ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Healthcare Applications ◽  
Smartphone Sensors

scholarly journals Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3021 ◽  
Author(s):  
Wonju Seo ◽  
Namho Kim ◽  
Sehyeon Kim ◽  
Chanhee Lee ◽  
Sung-Min Park
Keyword(s):  
Deep Learning ◽  
Mental Stress ◽  
Detection Algorithm ◽  
Workplace Stress ◽  
Physiological Signals ◽  
Work Related ◽  
Average Accuracy ◽  
Conventional Machine ◽  
End To End

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

scholarly journals Flexible and Highly Sensitive Pressure Sensors Based on Microstructured Carbon Nanowalls Electrodes

Nanomaterials ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 496 ◽  
Author(s):  
Xi Zhou ◽  
Yongna Zhang ◽  
Jun Yang ◽  
Jialu Li ◽  
Shi Luo ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
High Performance ◽  
Limit Of Detection ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Healthcare Applications ◽  
Highly Sensitive ◽  
Carbon Nanowalls ◽  
Pressure Regime

Wearable pressure sensors have attracted widespread attention in recent years because of their great potential in human healthcare applications such as physiological signals monitoring. A desirable pressure sensor should possess the advantages of high sensitivity, a simple manufacturing process, and good stability. Here, we present a highly sensitive, simply fabricated wearable resistive pressure sensor based on three-dimensional microstructured carbon nanowalls (CNWs) embedded in a polydimethylsiloxane (PDMS) substrate. The method of using unpolished silicon wafers as templates provides an easy approach to fabricate the irregular microstructure of CNWs/PDMS electrodes, which plays a significant role in increasing the sensitivity and stability of resistive pressure sensors. The sensitivity of the CNWs/PDMS pressure sensor with irregular microstructures is as high as 6.64 kPa−1 in the low-pressure regime, and remains fairly high (0.15 kPa−1) in the high-pressure regime (~10 kPa). Both the relatively short response time of ~30 ms and good reproducibility over 1000 cycles of pressure loading and unloading tests illustrate the high performance of the proposed device. Our pressure sensor exhibits a superior minimal limit of detection of 0.6 Pa, which shows promising potential in detecting human physiological signals such as heart rate. Moreover, it can be turned into an 8 × 8 pixels array to map spatial pressure distribution and realize array sensing imaging.

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

scholarly journals Towards Automatic and Interpretable Assignments of Patients Presenting with Pain to the Emergency Department

2021 ◽  
Author(s):  
J.A. Hughes ◽  
N.J. Brown ◽  
Thanh Vu ◽  
Anthony Nguyen
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Quality Of Care ◽  
Machine Learning Techniques ◽  
Initial Assessment ◽  
Common Symptom ◽  
Healthcare Applications ◽  
Learning Techniques ◽  
Real Time Identification

Introduction: Pain is the most common symptom that patients present with to the emergency department. It is hard to identify patients who have presented in pain to the emergency department when compliance with structured pain assessment is low. An ability to identify patients presenting in pain allows further investigation of the quality of care provided. Background: Machine and deep learning techniques are commonly used for text analysis in healthcare. Applications such as the classification of diagnosis and unplanned readmissions from textual medical records have previously been described. In other work, conventional and deep-learning techniques have demonstrated high performance in identifying patients presenting to the emergency department in pain. However, these models have lacked interpretability. Methods: This paper proposes the use of machine learning techniques to identify patients who present in pain based upon their initial assessment using interpretable deep learning models. Results: The interpretable deep learning model of pain identification was shown to have more accuracy and precision than other machine and deep learning techniques. This technique has significant application to large datasets for the identification of the quality of care and real-time identification of patients presenting in pain to improve their care.

Sign in / Sign up

Export Citation Format

Share Document