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Fortschritte in Der Nicht-Invasiven Biomedizinischen Signalverarbeitung Mit MLIn German Language: Dieses Buch stellt die modernen technologischen Fortschritte und Revolutionen im biomedizinischen Sektor vor. Fortschritte in der zeitgenössischen Sensorik, dem Internet der Dinge (IoT) und bei Maschinenlernalgorithmen und -architekturen haben neue Ansätze im mobilen Gesundheitswesen eingeführt. Eine kontinuierliche Beobachtung von Patienten mit kritischer Gesundheitssituation ist erforderlich. Sie ermöglicht die Überwachung ihres Gesundheitszustandes während alltäglicher Aktivitäten wie Sport, Gehen und Schlafen. Dank moderner IoT-Rahmenbedingungen und drahtloser biomedizinischer Implantate, wie Smartphones, Smartwatches und Gürtel, ist dies realisierbar. Solche Lösungen befinden sich derzeit in der Entwicklung und in Testphasen durch Gesundheits- und Regierungsinstitutionen, Forschungslabore und biomedizinische Unternehmen. Die biomedizinischen Signale wie Elektrokardiogramm (EKG), Elektroenzephalogramm (EEG), Elektromyographie (EMG), Phonokardiogramm (PCG), bei chronisch-obstruktiver Lungenkrankheit (COP) und Elektrookulographie (EoG), Photoplethysmographie (PPG), Positronenemissionstomographie (PET), Magnetresonanztomographie (MRI) und Computertomographie (CT) werden nicht-invasiv erfasst, gemessen und über die biomedizinischen Sensoren und Gadgets verarbeitet. Diese Signale und Bilder repräsentieren die Aktivitäten und Zustände des menschlichen kardiovaskulären, neuralen, visuellen und zerebralen Systems. Eine Mehrkanalerfassung dieser Signale und Bilder mit einer angemessenen Granularität ist für eine effektive Überwachung und Diagnose erforderlich. Sie erzeugt ein großes Datenvolumen, und seine Analyse ist manuell nicht machbar. Daher sind automatisierte Gesundheitssysteme in der Entwicklung. Diese Systeme basieren hauptsächlich auf der Erfassung und Sensorik von biomedizinischen Signalen und Bildern, Vorverarbeitung, Merkmalsextraktion und Klassifizierungsstufen. Die zeitgenössischen biomedizinischen Signal-Sensorik, Vorverarbeitung, Merkmalsextraktion und intelligente maschinelle und tiefgreifende Lernalgorithmen für die Klassifizierung werden beschrieben.
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Energy-Efficient and High-Performance IoT-Based WSN Architecture for Precision Agriculture Monitoring Using Machine Learning TechniquesTraditional irrigation systems for agricultural lands are expensive, time-consuming, and labor-intensive. Utilizing cutting-edge technology like machine learning, the internet of things, and wireless sensor networks, smart farming addresses current issues with agricultural sustainability while boosting the quantity and quality of crop production from the fields to fulfill the rising food demand. Soil moisture and temperature sensors are used to create a low-cost, real-time IoT-based automatic irrigation system. Two groups have been formed with the sensor information such as “require water” and “not require water” and saved on the server. The device intelligently determines whether the field needs water and automatically turns “ON” or “OFF” the motor. Machine learning based models such as k-nearest neighbor, support vector machines, decision tree, and naive bayes are applied to decide irrigation requirements. Performance metrics show that the KNN classifier performs better than the other two models. The suggested framework allows for better field monitoring and visualization.
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Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine LearningThis book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described.
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Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine LearningThe technological advancements have evolved the deployment of smart meters. A fine-grained metering data collection and analysis is necessary to bring benefits to multiple smart grid stakeholders. The classical sensing mechanism is time-invariant. Therefore, it results in the collection, transmission, and processing of a large amount of unnecessary data. This work employs the event-driven sensing mechanism to achieve real-time data compression. Afterward, the novel adaptive rate techniques are employed for the data conditioning, segmentation, and extraction of features. The pertinent features regarding the appliances’ consumption patterns are afterward used for their identification. It is realized by employing the mature Support Vector Machine and k-Nearest Neighbor classifiers. Results confirm a 3.4 times compression gain and the computational effectiveness of the suggested solution while securing 95.4% classification precision. It shows the benefits of integrating the proposed method in the realization of current energy efficiency services like enumerated consumption billing, effective load identification, and dynamic load management.
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Effective Power Quality Disturbances Identification Based on Event-Driven Processing and Machine LearningPower quality (PQ) disturbances cause rigorous issues in smart grids and industries. The identification of PQ disturbances and effective prevention of such events are essential. In this framework, vital aspects are a precise understanding and a real-time treatment of the PQ disturbances. A novel tactic is described in this chapter. It is founded on the basis of event-driven processing, analysis and machine learning for successful and efficient detection of PQ disturbances. The definition is based on an sophisticated combination of event-driven signal amplification and segmentation and local extraction of features and categorization to achieve an effective and appropriate precision method.Two rigorous classifiers namely k-Nearest Neighbor (KNN) and Naïve Bias are used for the automatic identification. For a case study, the framework functionality is checked, and findings are reported. Compared to conventional equivalents, the first order of magnitude reduction in the cumulative sample count is accomplished by the invented method. It leads to substantial benefit in the reduction of computational complexity and effectiveness in terms of power consumption and delays in processing. The designed framework also attains an appropriate precision of categorization for the case of four-class PQ disturbances.