Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The purpose of this report is to provide revised standards and guidelines for the exercise testing and training of individuals who are free from clinical manifestations of cardiovascular disease and those with known cardiovascular disease. These guidelines are intended for physicians, nurses, exercise physiologists, specialists, technologists, and other healthcare professionals involved in exercise testing and training of these populations. This report is in accord with the “Statement on Exercise” published by the American Heart Association (AHA).1

These guidelines are a revision of the 1995 standards of the AHA that addressed the issues of exercise testing and training.2 An update of background, scientific rationale, and selected references is provided, and current issues of practical importance in the clinical use of these standards are considered. These guidelines are in accord with the American College of Cardiology (ACC)/AHA Guidelines for Exercise Testing.3

### The Cardiovascular Response to Exercise

Exercise, a common physiological stress, can elicit cardiovascular abnormalities that are not present at rest, and it can be used to determine the adequacy of cardiac function. Because exercise is only one of many stresses to which humans can be exposed, it is more appropriate to call an exercise test exactly that and not a “stress test.” This is particularly relevant considering the increased use of nonexercise stress tests.

### Types of Exercise

Three types of muscular contraction or exercise can be applied as a stress to the cardiovascular system: isometric (static), isotonic (dynamic or locomotory), and resistance (a combination of isometric and isotonic).4,5 Isotonic exercise, which is defined as a muscular contraction resulting in movement, primarily provides a volume load to the left ventricle, and the response is proportional to the size of the working muscle mass and the intensity of exercise. Isometric exercise is defined as a muscular contraction without movement (eg, handgrip) and imposes greater pressure than volume …

Exercise Standards for Testing and Training A Scientific Statement From the American Heart Association

This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.

https://www.hpl.hp.com/techreports/2004/HPL-2004-229.pdf

Detecting stress during real-world driving tasks using physiological sensors

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions. Analysis of electrocardiograms using an end-to-end deep learning approach can detect and classify cardiac arrhythmia with high accuracy, similar to that of cardiologists.

/pdf/cardiologist-level-arrhythmia-detection-and-classification-12f54lgrtg.pdf

Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network

In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

Chapter 1. Introduction Chapter 2. The Electroencephalogram Chapter 3. EEG Signal Processing Chapter 4. Evoked Potentials Chapter 5. The Electromyogram Chapter 6. The Electrocardiogram Chapter 7. ECG Signal Processing Chapter 8. Heart Rate Variability Appendices Problems

Bioelectrical Signal Processing in Cardiac and Neurological Applications

Presents a QT database designed for evaluation of algorithms that detect waveform boundaries in the ECG. The database consists of 105 fifteen-minute excerpts of two-channel ECG Holter recordings, chosen to include a broad variety of QRS and ST-T morphologies. Waveform boundaries for a subset of beats in these recordings have been manually determined by expert annotators using an interactive graphic display to view both signals simultaneously and to insert the annotations. Examples of each morphology were included in this subset of annotated beats; at least 30 beats in each record, 3622 beats in all, were manually annotated in the database. In 11 records, two independent sets of annotations have been included, to allow inter-observer variability studies. The QT Database is available on a CD-ROM in the format previously used for the MIT-BIH Arrhythmia Database and the European ST-T Database, from which some of the recordings in the QT Database have been obtained.

/pdf/a-database-for-evaluation-of-algorithms-for-measurement-of-2zc0t5jrkb.pdf

A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG

Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database

In this paper we assessed the possibility of using the pulse rate variability (PRV) extracted from the photoplethysmography signal as an alternative measurement of the HRV signal in non-stationary conditions. The study is based on analysis of the changes observed during a tilt table test in the heart rate modulation of 17 young subjects. First, the classical indices of HRV analysis were compared to the indices from PRV in intervals where stationarity was assumed. Second, the time-varying spectral properties of both signals were compared by time-frequency (TF) and TF coherence analysis. Third, the effect of replacing PRV with HRV in the assessment of the changes of the autonomic modulation of the heart rate was considered. Time-invariant HRV and PRV indices showed no statistically significant differences (p > 0.05) and high correlation (>0.97). Time-frequency analysis revealed that the TF spectra of both signals were highly correlated (0.99 +/- 0.01); the difference between the instantaneous power, in the LF and HF bands, obtained from HRV and PRV was small (<10(-3) s(-2)) and their temporal patterns were highly correlated (0.98 +/- 0.04 and 0.95 +/- 0.06 in the LF and HF bands, respectively) and TF coherence in the LF and HF bands was high (0.97 +/- 0.04 and 0.89 +/- 0.08, respectively). Finally, the instantaneous power in the LF band was observed to significantly increase during head-up tilt by both HRV and PRV analysis. These results suggest that although some differences in the time-varying spectral indices extracted from HRV and PRV exist, mainly in the HF band associated with respiration, PRV could be used as a surrogate of HRV during non-stationary conditions, at least during the tilt table test.

https://iopscience.iop.org/article/10.1088/0967-3334/31/9/015/pdf

Pablo Laguna

Papers

A wavelet-based ECG delineator: evaluation on standard databases

Bioelectrical Signal Processing in Cardiac and Neurological Applications

A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG

Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database

Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions