Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

http://faratarjome.ir/u/media/shopping_files/store-EN-1427726230-2655.pdf

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent “validation” datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25 $\text {th}$ –75 $\text {th}$ percentiles for a window size of 32 seconds) of 1.5 (0.3–3.3) and 4.0 (1.8–5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.

/pdf/toward-a-robust-estimation-of-respiratory-rate-from-pulse-18l6fqmzl1.pdf

Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters

A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non-stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave-one-out cross-validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F 1 scores of 86.0%, 87.2%, and 79.1%, respectively.

/pdf/optimal-signal-quality-index-for-photoplethysmogram-signals-3hbamc64t5.pdf

Optimal Signal Quality Index for Photoplethysmogram Signals.

On an automatic delineator for arterial blood pressure waveforms

Information and computer technology popularizes in blood banks for its potentials in working efficiency as well as service quality. But, in the face of the explosive data and information, one of the challenges in blood bank information systems is how to make good use of them for decision making support. In this paper, we desire to explore the mechanisms of decision making support in blood bank information systems. Firstly, the properties of data and decisions in a blood bank are examined carefully; then, we introduce the development of computerized decision making support with special concerns on blood donation and transfusion service; finally, a case study is presented to evidence our understanding on decision making support in blood bank information systems.

On decision making support in blood bank information systems

After long-term exploration, it has been well established for the mechanisms of electrocardiogram (ECG) in health monitoring of cardiovascular system. Within the frame of an intelligent home healthcare system, our research group is devoted to researching/developing various mobile health monitoring systems, including the smart ECG interpreter. Hence, in this paper, we introduce an adaptive fuzzy ECG classifier with orientation to smart ECG interpreters. It can parameterize the incoming ECG signals and then classify them into four major types for health reference: Normal (N), Premature Atria Contraction (PAC), Right Bundle Block Beat (RBBB), and Left Bundle Block Beat (LBBB).

Ming Chui Dong

Papers

On an automatic delineator for arterial blood pressure waveforms

On decision making support in blood bank information systems

AFC-ECG: An Adaptive Fuzzy ECG Classifier

Development of a mobile pulse waveform analyzer for cardiovascular health monitoring

SIBAS: A blood bank information system and its 5-year implementation at Macau