Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients.

The pandemic has brought to everybody's attention the apparent need of remote monitoring, highlighting hitherto unseen challenges in healthcare. Today, mobile monitoring and real-time data collection, processing and decision-making, can drastically improve the cardiorespiratory-haemodynamic health diagnosis and care, not only in the rural communities, but urban ones with limited healthcare access as well. Disparities in socioeconomic status and geographic variances resulting in regional inequity in access to healthcare delivery, and significant differences in mortality rates between rural and urban communities have been a growing concern. Evolution of wireless devices and smartphones has initiated a new era in medicine. Mobile health technologies have a promising role in equitable delivery of personalized medicine and are becoming essential components in the delivery of healthcare to patients with limited access to in-hospital services. Yet, the utility of portable health monitoring devices has been suboptimal due to the lack of user-friendly and computationally efficient physiological data collection and analysis platforms. We present a comprehensive review of the current cardiac, pulmonary, and haemodynamic telemonitoring technologies. We also propose a novel low-cost smartphone-based system capable of providing complete cardiorespiratory assessment using a single platform for arrhythmia prediction along with detection of underlying ischaemia and sleep apnoea; we believe this system holds significant potential in aiding the diagnosis and treatment of cardiorespiratory diseases, particularly in underserved populations.

Assessing Generalization Ability of Majority Vote Point Classifiers.

Classification algorithms have been traditionally designed to simultaneously reduce errors caused by bias as well by variance. However, there occur many situations where low generalization error becomes extremely crucial to getting tangible classification solutions, and even slight overfitting causes serious consequences in the test results. In such situations, classifiers with low Vapnik-Chervonenkis (VC) dimension can bring out positive differences due to two main advantages: 1) the classifier manages to keep the test error close to training error and 2) the classifier learns effectively with small number of samples. This paper shows that a class of classifiers named majority vote point (MVP) classifiers, on account of very low VC dimension, can exhibit a generalization error that is even lower than that of linear classifiers. This paper proceeds by theoretically formulating an upper bound on the VC dimension of the MVP classifier. Later, through empirical analysis, the trend of exact values of VC dimension is estimated. Finally, case studies on machine fault diagnosis problems and prostate tumor detection problem revalidate the fact that an MVP classifier can achieve a lower generalization error than most other classifiers.