Fusing Continuous-valued Medical Labels using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78$\pm$0.63ms, significantly outperforming the best Challenge entry (15.37$\pm$2.13ms) as well as the EM, mean, and median voting strategies (14.76$\pm$0.52ms, 17.61$\pm$0.55ms, and 14.43$\pm$0.57ms respectively with $p<0.0001$)

A Spatio-Temporal Study of Ischemia and the Time-Frequency Coupling Variations between the ST Amplitude, Heart Rate and Dominant Angle

Abstract An analysis of the Long Term ST Database (LTSTDB) was conducted to quantify the spatio-temporal dynamics of ischemic and non-ischemic episodes. For all 86 recordings the ischemic episode length is decribed by a lognormal distribution and the non-ischemic episode length by a generalized extreme value distribution. For the 15 recordings that possess orthogonal (EASI) leads sets we derived the 12 standard leads and analyzed the spatial time course (from the j-point to j+120 ms) of each episode over time to identify dominant trends. Although the magnitude of the ischemic episodes did not reveal any inter-subject trend (except for generally exhibiting Brownian-like motion), there appeared to be strong correlations with the heart rate (HR). Wavelet cross-spectral coupling with significance testing was then applied to the ST-amplitude and HR evolution over the course of each episode. In all subjects significant cross-spectral correlations were found at very low frequencies (&lt;0.04 Hz), as well as at respiration and baroreflex frequencies. This may indicate that the ischemic episodes are modulated by blood pressure and activity or HR-related phenomena and that all episodes in the LTSTDB may be of a &apos;mixed&apos; type at some point in their duration. The dominant angle also showed significant correlation (p&lt;0.01) with the ST amplitude and HR changes at similar frequencies to those described above. All three protocols used to define ischemia in the LTSTDB gave similar results. Introduction Modelling the short-and long-term spatial and temporal changes in the ECG during ischemia provides a mechanism for baseline testing of relevant signal processing algorithms. Our aim in this work was to provide a description of such changes in order to provide information to build an accurate simulation of the ECG during ischemia. Methods Data The data used in this study were taken from the LongTerm ST Database (LTSTDB) [1] available from PhysioNet which contains 21-24 hour multi-channel ECG recordings and annotated ischemic and non-ishemic ST changes. An ischemic episode was defined to start when the ST deviation exceeded a lower threshold, V lower = 50µV . Next, the deviation was required to reach or exceed an upper threshold, V upper , for at least a continuous interval of T min seconds. Finally, the episode ended when the deviation dropped to less than V lower = 50µV in the following T sep = 30 s. The values of (V upper , T min ) were (75 µV , 30 s), (100 µV , 30 s) and (100 µV , 60 s) defined as protocol STA, STB and STC respectively. All analysis was performed in the vectorcardiogram (VCG) space. The LTSTDB contains 15 recordings (s30691 through s30801) which used the EASI lead system Similarly they give the transformation from the EASI configuration to the vectorcardiogram (X, Y, Z) configuration, V , the matrix Temporal analysis of the episodes The lengths of each ischemic and non-ischemic episode for all 86 patients in the LTSTDB were calculated using all available leads. An episode was taken to start if any lead satisfied the criteria for the relevant protocol and was taken to end when all leads ceased to satisfy the same pro

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been driven predominantly by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has brought up significant concerns, regarding the accuracy of reported BP values across settings. In this survey, focusing mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices which use artificial-intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and to provide individualized BP-related cardiovascular risk indexes