Statistical considerations for testing automated sphygmomanometers in special patient populations.

The International Standards Organization 81060-2:2018 is the current global Standard for the validation of automated sphygmomanometers. It specifies the requirements for clinical studies on the general population, as well as additional requirements for special populations, which might have physiologic characteristics that affect the accuracy of blood pressure measurements. This paper summarizes the statistical methodology behind the sample size required to test automated sphygmomanometers in these special populations and specifically addresses the pregnant patient population.

A new noninvasive finger sensor (NICCI system) for continuous blood pressure and pulse pressure variation monitoring: A method comparison study in patients having neurosurgery.

Background: The NICCI system (Getinge, Gothenburg, Sweden) is a new noninvasive haemodynamic monitoring system using a finger sensor.

Objectives: We aimed to investigate the performance of the NICCI system to measure blood pressure and pulse pressure variation compared with intra-arterial measurements.

Design: A prospective method comparison study.

Right ventricular and pulmonary artery pulse pressure variation and systolic pressure variation for the prediction of fluid responsiveness: an interventional study in coronary artery bypass surgery patients.

Purpose: Predicting fluid responsiveness is essential when treating surgical or critically ill patients. When using a pulmonary artery catheter, pulse pressure variation and systolic pressure variation can be calculated from right ventricular and pulmonary artery pressure waveforms.

Methods: We conducted a prospective interventional study investigating the ability of right ventricular pulse pressure variation (PPV) and systolic pressure variation (SPV) as well as pulmonary artery pulse pressure variation (PPV) and systolic pressure variation (SPV) to predict fluid responsiveness in coronary artery bypass (CABG) surgery patients.

A novel art of continuous noninvasive blood pressure measurement.

Wearable sensors to continuously measure blood pressure and derived cardiovascular variables have the potential to revolutionize patient monitoring. Current wearable methods analyzing time components (e.g.

Agreement between continuous noninvasive finger cuff-derived and invasive arterial blood pressure measurements: Effect of data sampling and data processing.

Background: The effect of different methods for data sampling and data processing on the results of comparative statistical analyses in method comparison studies of continuous arterial blood pressure (AP) monitoring systems remains unknown.

Objective: We sought to investigate the effect of different methods for data sampling and data processing on the results of statistical analyses in method comparison studies of continuous AP monitoring systems.

Design: Prospective observational study.

Changes in haemodynamic and autonomous nervous system parameters measured non-invasively throughout normal pregnancy.

Objective: To examine non-invasively haemodynamic and autonomous parameters throughout normal pregnancy.

Study Design: We used the Task Force Monitor 3040i system to retrieve, record, and calculate haemodynamic as well as autonomous parameters. 20 healthy women were included and scheduled for longitudinal examinations throughout normal pregnancy.

Non-invasive beat-to-beat cardiac output monitoring by an improved method of transthoracic bioimpedance measurement.

The report describes a method of impedance cardiography using an improved estimate of thoracic volume. The formulas and their implementation in hardware and software are explained and new shortband electrodes are described which generate a good homogeneous thoracic field. Examples of stroke volume and cardiac output curves underline the capabilities of the monitoring system "Task Force Monitor".

Feature extraction for on-line EEG classification using principal components and linear discriminants.

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement).

EEG-based discrimination between imagination of right and left hand movement.

Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was > 10 s. Each subject imagined a total of 160 hand movements in each of 3-4 sessions (training) without feedback and 7-8 sessions with feedback.

Timing of EEG-based cursor control.

Recent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control.

Adaptive autoregressive modeling used for single-trial EEG classification.

An adaptive autoregressive (AAR) model is used for analyzing event-related EEG changes. Such an AAR model is applied to single EEG trials of three subjects, recorded over both sensorimotor areas during imagination of left and right hand movements. It is found that discrimination between both types of motor-imagery is possible using linear discriminant analysis, but the time point for optimal classification is different in each subject.

On-line EEG classification during externally-paced hand movements using a neural network-based classifier.

EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line.

Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns.

The paper describes work on the brain--computer interface (BCI). The BCI is designed to help patients with severe motor impairment (e.g.

Sleep classification in infants by decision tree-based neural networks.

This paper presents an AI-based approach to automatic sleep stage scoring. The system TBNN (Tree-Based Neural Network) uses a decision-tree generator to provide knowledge that defines the architecture of a backpropagation neural network, including feature selection and initialisation of the weights. The case study reports a successful application to the data from polygraphic all-night sleep of 8 babies aged 6 months.

EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.

Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen.

Selection of electrode positions for an EEG-based brain computer interface (BCI).

One major question in designing an EEG-based Brain Computer Interface to bypass the normal motor pathways is the selection of proper electrode positions. This study investigates electrode selection with a Distinction Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an extended Learning Vector Quantizer (LVQ) which employs a weighted distance function for dynamical scaling and feature selection.

Differentiation between finger, toe and tongue movement in man based on 40 Hz EEG.

Movements of right and left index fingers, right toe and tongue were studied by EEG measurement in the alpha and gamma (30-40 Hz) bands. The EEG was recorded with a 56-electrode array over pre- and postcentral areas. For each movement the average power decrease, as a measurement of the event-related desynchronization or power increase in narrow frequency bands, was calculated.

AI-based approach to automatic sleep classification.

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.

Towards automated sleep classification in infants using symbolic and subsymbolic approaches.

The paper addresses the problem of automatic sleep classification. A special effort is made to find a method of extracting reasonable descriptions of the individual sleep stages from sample measurements of EGG, EMG, EOG, etc., and from a classification of these measurements provided by an expert.

EEG classification by learning vector quantization.

EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface (BCI) built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp. The method of classification with LVQ is described in detail along with first results on a subject who participated in four on-line cursor control sessions. Using this data, extensive off-line experiments were performed to show the influence of the various parameters of the classifier and the extracted features of the EEG on the classification results.

Sleep classification in infants based on artificial neural networks.

The study reports on the possibility of classifying sleep stages in infants using an artificial neural network. The polygraphic data from 4 babies aged 6 weeks, 6 months and 1 year recorded over 8 hours were available for classification. From each baby 22 signals were recorded, digitized and stored on an optical disc.

Prediction of the side of hand movements from single-trial multi-channel EEG data using neural networks.

Thirty channels of EEG data were recorded prior to voluntary right or left hand movements. Event-related desynchronization (ERD) was quantified in the 8-10 Hz and 10-12 Hz bands in single-trial data and used as training input for a neural network comprised of a learning vector quantizer (LVQ). After a training period, the network was able to predict the side of hand movement from single-trial EEG data recorded prior to movement onset.