João S. Domingos

A review of current sleep screening applications for smartphones

Sleep disorders are a common problem and contribute to a wide range of healthcare issues. The societal and financial costs of sleep disorders are enormous. Sleep-related disorders are often diagnosed with an overnight sleep test called a polysomnogram, or sleep study involving the measurement of brain activity through the electroencephalogram. Other parameters monitored include oxygen saturation, respiratory effort, cardiac activity (through the electrocardiogram), as well as video recording, sound and movement activity. Monitoring can be costly and removes the patients from their normal sleeping environment, preventing repeated unbiased studies. The recent increase in adoption of smartphones, with high quality on-board sensors has led to the proliferation of many sleep screening applications running on the phone. However, with the exception of simple questionnaires, no existing sleep-related application available for smartphones is based on scientific evidence. This paper reviews the existing smartphone applications landscape used in the field of sleep disorders and proposes possible advances to improve screening approaches.

Low-Cost Blood Pressure Monitor Device for Developing Countries

Taking the Blood Pressure (BP) with a traditional sphygmomanometer requires a trained user. In developed countries, patients who need to monitor their BP at home usually acquire an electronic BP device with an automatic inflate/deflate cycle that determines the BP through the oscillometric method. For patients in resource constrained regions automated BP measurement devices are scarce because supply channels are limited and relative costs are high. Consequently, routine screening for and monitoring of hypertension is not common place. In this project we aim to offer an alternative strategy to measure BP and Heart Rate (HR) in developing countries. Given that mobile phones are becoming increasingly available and affordable in these regions, we designed a system that comprises low-cost peripherals with minimal electronics, offloading the main processing to the phone. A simple pressure sensor passes information to the mobile phone and the oscillometric method is used to determine BP and HR. Data are then transmitted to a central medical record to reduce errors in time stamping and information loss.

A scalable mHealth system for noncommunicable disease management

Barriers to effective screening and management of NCDs in resource-constrained regions include limited availability of trained personnel, access to affordable automatic medical devices, and longitudinal clinical data. We present an end-to-end mHealth system which takes advantage of the almost universal availability of smartphones in order to address these barriers in a scalable and affordable manner. Our system includes simple, low-cost (

Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device.

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Structured Random Forests for Myocardium Delineation in 3D Echocardiography

20) and open-source peripherals that allow a minimally trained person to collect high-quality medical data at the point-of-care through a standard smartphone; allows the reliable transmission of clinical data even in the case of high-latency network connections; stores data in a cloud-based system, making patient records accessible anywhere; and enables both crowdsourced diagnostics and generation of annotated data for the research and development of automatic decision support and risk assessment systems. We show examples of the different elements of the system tailored for the management of cardiovascular disease and chronic obstructive pulmonary disease, which includes prototypes of the low-cost peripherals. In a validation study (of 40 volunteers), our smartphone-based blood pressure (BP) monitor was shown to measure BP, heart rate and respiration rate with a mean-absolute-error of less than 5 units from the reference values for 80% of the measurements.

Local Phase-Based Fast Ray Features for Automatic Left Ventricle Apical View Detection in 3D Echocardiography

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeters photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). We propose a novel method that combines the information from the two respiratory-induced variations (frequency and amplitude) via frequency analysis to both estimate RR and eliminate estimations considered to be unreliable because of poor signal quality. The method was evaluated using data acquired from 40 subjects containing ECG, respiration and blood pressure waveforms, the latter acquired using an in-house built blood pressure device that is able to connect to a mobile phone. Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.

Local phase-based fast ray features for automatic left ventricle apical view detection in 3d echocardiography

Delineation of myocardium borders from 3D echocardiography is a critical step for the diagnosis of heart disease. Following the approach of myocardium segmentation as a contour finding task, recent work has shown effective methods to interpret endocardial edge information in the left ventricle. Nevertheless, these methods are still prone to preserve irrelevant edge responses and would struggle to overcome chief ventricle anatomical challenges. In this paper we adapt Structured Random Forests, borrowed from computer vision, for fast and robust myocardium edge detection. This method is evaluated on a dataset composed of short-axis slices from 25 End-Diastolic echocardiography volumes. Results show that the proposed ensemble model outperforms standard intensity-based and local phase-based edge detectors, while removing or significantly suppressing irrelevant edges triggered by ultrasound image artefacts and blood pool anatomical structures.

Quantification of cardiac bull's-eye map based on principal strain analysis for myocardial wall motion assessment in stress echocardiography

3D echocardiography is an imaging modality that enables a more complete and rapid cardiac function assessment. However, as a time-consuming procedure, it calls upon automatic view detection to enable fast 3D volume navigation and analysis. We propose a combinatorial model- and machine learning-based left ventricle (LV) apical view detection method consisting of three steps: first, multiscale local phase-based 3D boundary detection is used to fit a deformable model to the boundaries of the LV blood pool. After candidate slice extraction around the derived mid axis of the LV segmentation, we propose the use of local phase-based Fast Ray features to complement conventional Haar features in an AdaBoost-based framework for automated standardized LV apical view detection. Evaluation performed on a combination of healthy volunteers and clinical patients with different image quality and ultrasound probes show that apical plane views can be accurately identified in a 360 degree swipe of 3D frames.