Redesigning Social Inquiry Fuzzy Sets And Beyond

第１章 ＧＥＭ調査の概要（分析の枠組み；調査方法；起業活動の定義；起業活動率；起業活動と経済成長；起業の計画と失敗） 第２章 起業家と事業特性（起業家の背景；起業家の能力；事業特性；起業家教育） 第３章 起業の環境（社会的資源；起業家に対する評価；経済危機の影響；起業活動の投資環境） 第４章 専門家調査（資金調達；政府の方針；支援プログラム；教育システム；技術移転；コマーシャル・サービス；起業文化；事業機会；経営能力；起業家に対する評価；女性への支援；急成長への注目；イノベーションへの関心；調査結果） 第５章 政策への提

起業と経済成長 : global entrepreneurship monitor調査報告

Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses.

On the Analysis of Fingertip Photoplethysmogram Signals

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal's second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

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A review on wearable photoplethysmography sensors and their potential future applications in health care.

A novel method for detecting R-peaks in electrocardiogram (ECG) signal

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

/pdf/efficient-prediction-of-cardiovascular-disease-using-machine-1g3efyu5h4.pdf

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

In this paper, we investigate the QRS frequency bands in ECG signals. Any QRS detection algorithm accuracy depends on the frequency range of ECG being processed. The QRS complex has different morphology and frequency band for different arrhythmias and noises in ECG signals. A standard bandpass range that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio will be useful in ECG monitoring and diagnostic tools. A sensitive QRS detection algorithm has been introduced to compare the performance of using different frequency bands. The results shows that the recommended bandpass frequency range for detecting QRS complexes is 8-20Hz which the best signal-to-noise ratio.

Frequency bands effects on qrs detection

Accurate detection of QRS complexes is important for ECG signal analysis. In this paper, a generic algorithm using Coiflet wavelets is introduced to improve the detection of QRS complexes in Arrhythmia ECG Signals that suffer from: 1) non-stationary effects, 2) low Signal-to-Noise Ratio, 3) negative QRS polarities, 4) low QRS amplitudes, and 5) ventricular ectopics. The algorithm achieves high detection rates by using a signal-to-noise ratio threshold instead of predetermined static thresholds. The performance of the algorithm was tested on 48 records of the MIT/BIH Arrhythmia Database. It was shown that this adaptive approach results in accurate detection of the QRS complex and that Coiflet1 achieves better detection rate than the other Coiflet wavelets.

R wave detection using Coiflets wavelets

With the application of Internet of Things technology to every aspect of life, the potential damage caused by Internet of things attacks is more serious than for traditional network attacks. Traditional intrusion detection systems do not serve the network environment of the IoT very well, so it is important to study intrusion detection systems suitable for the network environment of the Internet of Things. Researchers have found that the combination of machine learning technologies with an intrusion detection system is an effective way to resolve the drawbacks traditional IDSs have when they are used for IoT. This research involves the design of a novel intrusion detection system and the implementation and evaluation of its analysis model. This new intrusion detection system uses a hybrid placement strategy based on a multi-agent system. The new system consists of a data collection module, a data management module, an analysis module and a response module. For the implementation of the analysis module, this research applies a deep neural network algorithm for intrusion detection. The results demonstrate the efficiency of deep learning algorithms for detecting attacks from the transport layer. Compared with traditional detection methods used in IDSs, the analysis indicates that deep learning algorithms are more suitable for intrusion detection in an IoT network environment.

Intrusion Detection System for Internet of Things based on a Machine Learning approach

The accurate detection of QRS complexes is important for ECG signal analysis. This paper presents an improved version of a QRS detector based on an adaptive quantized threshold. The algorithm achieves high detection rates by using automatic thresholds instead of predetermined static thresholds. We improved the number of detected QRS in non-stationary random arrhythmic ECG signals by applying a secondary threshold. The performance of the algorithm was tested on 19 records of the MIT/BIH Arrhythmia Database resulting in 97.5% sensitivity and 99.9% positive predictivity.

Friso De Boer

Papers

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Frequency bands effects on qrs detection

R wave detection using Coiflets wavelets

Intrusion Detection System for Internet of Things based on a Machine Learning approach

Improved QRS Detection Algorithm using Dynamic Thresholds