AIMC Topic: Machine Learning

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Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule...

Heart beat characterization from ballistocardiogram signals using extended functions of multiple instances.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A multiple instance learning (MIL) method, extended Function of Multiple Instances (eFUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The goal of this approach is to learn a personalized heartbeat "concept" fo...

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was ...

Intelligent hearing aids: the next revolution.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The first revolution in hearing aids came from nonlinear amplification, which allows better compensation for both soft and loud sounds. The second revolution stemmed from the introduction of digital signal processing, which allows better programmabil...

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.

The Review of scientific instruments
Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of ...

Evaluation of a Technology-Based Adaptive Learning and Prevention Program for Stress Response-A Randomized Controlled Trial.

Military medicine
To prevent deployment-related disorders, Chaos Driven Situations Management Retrieval System (CHARLY), a computer-aided training platform with a biofeedback interface has been developed. It simulates critical situations photorealistic for certain tar...

Being replaced by a robot.

The British journal of general practice : the journal of the Royal College of General Practitioners

Artificial Neural Network Approach in Laboratory Test Reporting:  Learning Algorithms.

American journal of clinical pathology
OBJECTIVES: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would e...

Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches.

Statistical applications in genetics and molecular biology
Modern biological experiments often involve high-dimensional data with thousands or more variables. A challenging problem is to identify the key variables that are related to a specific disease. Confounding this task is the vast number of statistical...

Validity of an Integrative Method for Processing Physical Activity Data.

Medicine and science in sports and exercise
UNLABELLED: Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions.