IEEE-EMBS International Conference on Biomedical and Health Informatics 

About: IEEE-EMBS International Conference on Biomedical and Health Informatics is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Deep learning. Over the lifetime, 953 publications have been published by the conference receiving 7517 citations.

Smartwatch-based activity recognition: A machine learning approach

Abstract: Smartwatches and smartphones contain accelerometers and gyroscopes that sense a user's movements, and can help identify the activity a user is performing. Research into smartphone-based activity recognition has exploded over the past few years, but research into smartwatch-based activity recognition is still in its infancy. In this paper we compare smartwatch and smartphone-based activity recognition, and smartwatches are shown to be capable of identifying specialized hand-based activities, such as eating activities, which cannot be effectively recognized using a smartphone (e.g., smartwatches can identify the "drinking" activity with 93.3% accuracy while smartphones achieve an accuracy of only 77.3%). Smartwatch-based activity recognition can form the basis of new biomedical and health applications, including applications that automatically track a user's eating habits.

Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

Abstract: This paper studied automatic identification of malaria infected cells using deep learning methods. We used whole slide images of thin blood stains to compile an dataset of malaria-infected red blood cells and non-infected cells, as labeled by a group of four pathologists. We evaluated three types of well-known convolutional neural networks, including the LeNet, AlexNet and GoogLeNet. Simulation results showed that all these deep convolution neural networks achieved classification accuracies of over 95%, higher than the accuracy of about 92% attainable by using the support vector machine method. Moreover, the deep learning methods have the advantage of being able to automatically learn the features from the input data, thereby requiring minimal inputs from human experts for automated malaria diagnosis.

A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology

Abstract: Atrial Fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, with a prevalence of 2% in the community. Not only it is associated with reduced quality of life, but also increased risk of stroke and myocardial infarction. Unfortunately, many cases of AF are clinically silent and undiagnosed, but long-term monitoring is difficult. Nonetheless, efforts at monitoring at-risk individuals and detecting clinically silent AF may yield significant public health benefit, as individuals with new-onset, asymptomatic AF would receive preventive therapies with anticoagulants and beta-blockers, for example. Wearables have enormous potential to provide low-risk and low-cost long-term monitoring of AF, but signals from such devices suffer from significant movement related noise that resembles AF. This work presents a robust approach to AF detection in a sequence of short windows with significant movement artifact. Pulsatile photoplethysmographic (PPG) data and triaxial accelerometry from 98 subjects (45 with AF and 53 with other rhythms) were captured using a multichannel wrist-worn device. A single channel electrocardiogram (ECG) was recorded (for rhythm verification only) simultaneously. A novel deep neural network approach to classify AF from wrist-worn PPG signals was developed on this data. A continuous wavelet transform was applied to the PPG data and a convolutional neural network (CNN) was trained on the derived spectrograms to detect AF. Combining the output of the CNN with features calculated based on beat-to-beat variability and signal quality provided a significant accuracy boost. Leave-one-out cross validation resulted in a pooled AUC of 0.95 (Accuracy=91.8%). The proposed approach resulted in a novel robust and accurate algorithm for detection of AF from PPG data, which is scalable and likely to improve in accuracy as the dataset size continues to expand.

Long-term blood pressure prediction with deep recurrent neural networks

Abstract: Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.

Fall detection by built-in tri-accelerometer of smartphone

Abstract: In this study, a fall detection system based on the data acquired from a waist-mounted smartphone has been developed in a real-time environment. The built-in tri-accelerometer was utilized to collect the information about body movement. At the same time, the smartphone is able to classify the data for activity recognition. Body motion can be classified into five different patterns, i.e. vertical activity, lying, sitting or static standing, horizontal activity and fall. If a fall is suspected, an automatic Multimedia Messaging Service (MMS) will be sent to pre-selected people, with information including the time, GPS coordinate, and Google map of suspected fall location. The major advantage of the proposed system is the use of smartphone which is readily available to most people.