Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope.

OBJECTIVE: Falling is an important health maintenance issue for the elderly and people with movement disorders, strokes and multiple sclerosis. With the development of light, low-cost wearable technology, inertia-based fall detection has gained much attention. However, some large movements, such as jumping and postural changes, are frequently confounded with falls. For example, commonly used fall detection methods based on acceleration amplitude produce a large number of false alerts unless they are combined with post-fall posture identification. In this paper, we propose two new inertial parameters to improve the selectivity of threshold-based fall detection methods, and evaluate strategies to distinguish falls from other activities of daily life (ADLs). APPROACH: We define two new inertial parameters, acceleration cubic-product-root magnitude (ACM) and angular velocity cubic-product-root magnitude (AVCM). Along with acceleration magnitude (AM), we test threshold-based fall detection methods based on single parameters and combinations. We collected inertial data on four types of simulated falls and eight types of ADLs from a study with 15 participants wearing a chest-mounted sensor with accelerometer and gyroscope. Two public datasets, UMAFall and Cognent Labs, were also included to evaluate fall detection methods. MAIN RESULTS: We chose the detection threshold with 99% sensitivity and the best possible specificity. The hybrid of AM, ACM and AVCM method had a lower rate of misclassification than single-parameter methods. Leave-one-out cross-validation shows that the hybrid fall detection method can achieve both high specificity and high sensitivity. SIGNIFICANCE: Using multiple inertial parameters improves the specificity of fall detection.

Brain connectivity of patients with Alzheimer's disease by coherence and cross mutual information of electroencephalograms during photic stimulation.

Alzheimer's disease (AD) is a neurodegenerative disease, usually diagnosed by neuropsychological tests, and excluded from other cerebral diseases by brain images. An electroencephalogram (EEG) provides a means of disclosing the reduced functional couplings between brain regions that occurs with AD. In the present study, 16 probable AD patients and 15 age-matched, gender-matched normal subjects were enrolled. Spectral coherence and cross mutual information (CMI) were used to analyze EEGs during intermittent photic stimulation (PS). Ocular- and heartbeat-related source components (SCs) obtained from multi-channel EEGs by the independent component analysis were discarded, and the photic-related SCs were reduced using a comb filter. The undisturbed SCs and photic-related SCs before and after photic reduction were used to reconstruct photic-preserved EEGs and photic-reduced EEGs, from which harmonic coherences (direct photic-driving response) and rhythmic coherences and CMI (indirect photic affection) were computed, respectively. Our results indicate that the rhythmic coherences (particularly in the alpha and beta bands) and CMI variables as well as the harmonic coherences (particularly related to 3-Hz PS) were significantly lower in the probable AD than in normal subjects, whereas the variables derived from the resting EEGs were not statistically significant. This finding implied that the variables obtained during PS could be used to disclose impaired intra-brain associations in probable AD.

An intelligent classifier for prognosis of cardiac resynchronization therapy based on speckle-tracking echocardiograms.

PURPOSE: Predicting response after cardiac resynchronization therapy (CRT) has been a challenge of cardiologists. About 30% of selected patients based on the standard selection criteria for CRT do not show response after receiving the treatment. This study is aimed to build an intelligent classifier to assist in identifying potential CRT responders by speckle-tracking radial strain based on echocardiograms. METHODS AND MATERIALS: The echocardiograms analyzed were acquired before CRT from 26 patients who have received CRT. Sequential forward selection was performed on the parameters obtained by peak-strain timing and phase space reconstruction on speckle-tracking radial strain to find an optimal set of features for creating intelligent classifiers. Support vector machine (SVM) with a linear, quadratic, and polynominal kernel were tested to build classifiers to identify potential responders and non-responders for CRT by selected features. RESULTS: Based on random sub-sampling validation, the best classification performance is correct rate about 95% with 96-97% sensitivity and 93-94% specificity achieved by applying SVM with a quadratic kernel on a set of 3 parameters. The selected 3 parameters contain both indexes extracted by peak-strain timing and phase space reconstruction. CONCLUSIONS: An intelligent classifier with an averaged correct rate, sensitivity and specificity above 90% for assisting in identifying CRT responders is built by speckle-tracking radial strain. The classifier can be applied to provide objective suggestion for patient selection of CRT.

Phase space analysis of myocardial coordination related to left ventricular ejection fraction by echocardiographic speckle-tracking radial strain.

Phase space reconstruction, which is performed by converting echocardiogram-derived strain data from different ventricular regions into phase space trajectories, is applied in this study to describe nonlinear behaviour of myocardial coordination. A new method was developed to quantify patterns of phase space trajectories. Echocardiograms of 31 healthy individuals and 63 patients with left bundle branch block (LBBB) and different left ventricular ejection fractions (LVEFs) were used to evaluate this method. The LBBB patients were separated into two groups: LBBB with a LVEF≥50% and LBBB with a LVEF<50%. LVEF is used to represent levels of systolic function and disease severity. A classifying map was constructed to separate the reconstructed phase space into three zones and to acquire the parameters Pz1, Pz2 and Pz3 as percentiles of phase points related to the zones. The criteria used to define the zones were cross-tested. Based on these parameters, significant group-related differences in myocardial coordination were observed. Significantly smaller Pz1 and significantly larger Pz2 values were observed in the healthy group, as compared to the patient group, and similar, significant results were obtained for the patients with LVEF≥50%, as compared to the patients with LVEF<50% (p<.05). A significantly larger Pz3 was observed in patients with LVEF<50%, as compared to the other patients (p<.05). In addition, different inter-regional relationships among strain pairs (all, near-side, middle-side and opposite-wall) were examined to create phase space trajectories. Consistent group-related differences were observed when different inter-regional relationships were applied. Expanding the use of the proposed method to assess various pathological factors and therapeutic impacts is promising.

Patterns of left ventricular contraction in strain vector space related to bundle branch block with heart failure by speckle-tracking echocardiography.

The aim of this research is to study bundle branch block (BBB)-related patterns of radial strain in the left ventricles of patients with heart failure by speckle-tracking echocardiography. Twenty-seven left-BBB (LBBB), 10 right-BBB (RBBB), and 11 narrow QRS-complexes (non-BBB) patients and 11 healthy subjects were assessed. Strain fractions used to quantify thickening-during-systole and thinning-during-diastole, and timing parameters defined as time to onset-of-thickening and peak-strain were measured. Principal strain vectors were conducted on the fractions and parameters to analyze mechanical discoordination and dyssynchrony. Heart failure patients show a significantly greater extent of discoordination and dyssynchrony compared with healthy subjects. Significant differences between the LBBB and RBBB groups are demonstrated by deflection, a spatial characteristic of myocardial coordination. New information provided by these findings can provide a better understanding of BBB-related mechanisms of myocardial coordination and may be useful in improving patient selection, electrode placement and subsequent outcomes for cardiac resynchronization therapy.

Recognition of ventricular extrasystoles over the reconstructed phase space of electrocardiogram.

Distinguishing ventricular extrasystoles from normal heartbeats is crucial to cardiac arrhythmia analysis. This paper proposes novel morphological descriptors, the major portrait partition area (MPPA) and point distribution percentage (PDP), which are extracted from the reconstructed phase space of the QRS complex. These measures can be linked to QRS width and prolonged ventricular contraction, and offer several advantages over traditional characterization of the QRS structure: it does not require QRS boundary detection, is robust under R-peak misalignment, and including some information from nearby points. The first four principal components of MPPA variables and PDPs in the first and the third quadrants of the phase space diagram were used as inputs of neural networks. The performance of networks in distinguishing premature ventricular contraction events from normal heartbeats were evaluated under a series of 50 cross-validations based on the electrocardiogram data taken from the MIT/BIH arrhythmia database. The sensitivity and specificity obtained using the aforementioned MPPA principal components and PDPs as inputs were similar to those obtained using wavelet features and Hermite coefficients. However, the phase space information performed better in situations of noise contaminations and waveform deformations.

Complex analysis of neuronal spike trains of deep brain nuclei in patients with Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been used to alleviate symptoms of Parkinson's disease. During image-guided stereotactic surgery, signals from microelectrode recordings are used to distinguish the STN from adjacent areas, particularly from the substantia nigra pars reticulata (SNr). Neuronal firing patterns based on interspike intervals (ISI) are commonly used. In the present study, arrival time-based measures, including Lempel-Ziv complexity and deviation-from-Poisson index were employed. Our results revealed significant differences in the arrival time-based measures among non-motor STN, motor STN and SNr and better discrimination than the ISI-based measures. The larger deviations from the Poisson process in the SNr implied less complex dynamics of neuronal discharges. If spike classification was not used, the arrival time-based measures still produced statistical differences among STN subdivisions and SNr, but the ISI-based measures only showed significant differences between motor and non-motor STN. Arrival time-based measures are less affected by spike misclassifications, and may be used as an adjunct for the identification of the STN during microelectrode targeting.

A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration.

Falling is an important problem in the health maintenance of people above middle age. Portable accelerometer systems have been designed to detect falls. However, false alarms induced by some dynamic motions, such as walking and jumping, are difficult to avoid. Acceleration cross-product (AC)-related methods are proposed and examined by this study to seek solutions for detecting falls with less motion-evoked false alarms. A set of tri-axial acceleration data is collected during simulated falls, posture transfers and dynamic activities by wireless sensors for making methodological comparisons. The performance of fall detection is evaluated in aspects of parameter comparison, threshold selection, sensor placement and post-fall posture (PP) recruitment. By parameter comparison, AC leads to a larger area under the receiver operating characteristic (ROC) curve than acceleration magnitude (AM). Three strategies of threshold selection, for 100% sensitivity (Sen100), for 100% specificity (Spe100) and for the best sum (BS) of sensitivity and specificity, are evaluated. Selecting a threshold based on Sen100 and BS leads to more practicable results. Simultaneous data recording from sensors in the chest and waist is performed. Fall detection based on the data from the chest shows better global accuracy. PP recruitment leads to lower false alarm ratios (FR) for both AC- and AM-based methods.

Detection of neuronal spikes using an adaptive threshold based on the max-min spread sorting method.

Neuronal spike information can be used to correlate neuronal activity to various stimuli, to find target neural areas for deep brain stimulation, and to decode intended motor command for brain-machine interface. Typically, spike detection is performed based on the adaptive thresholds determined by running root-mean-square (RMS) value of the signal. Yet conventional detection methods are susceptible to threshold fluctuations caused by neuronal spike intensity. In the present study we propose a novel adaptive threshold based on the max-min spread sorting method. On the basis of microelectrode recording signals and simulated signals with Gaussian noises and colored noises, the novel method had the smallest threshold variations, and similar or better spike detection performance than either the RMS-based method or other improved methods. Moreover, the detection method described in this paper uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data (as multi-electrode recordings).

Classification of neuronal spikes over the reconstructed phase space.

Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.

Correlates of the shift in heart rate variability with postures and walking by time-frequency analysis.

Heart rate (HR) variability derived from electrocardiogram (ECG) can be used to assess the function of the autonomic nervous system. HR exhibits various characteristics during different physical activities attributed to the altered autonomic mediation, where it is also beneficial to reveal the autonomic shift in response to physical-activity change. In this paper, the physical-activity-related HR behaviors were delineated using a portable ECG and body acceleration recorder based on a personal digital assistant and the smoothed pseudo Wigner-Ville distribution. The results based upon eighteen subjects performing four sequential 5-min physical activities (supine, sitting, standing and spontaneous walking) showed that the high-frequency heartbeat fluctuations during supine and sitting were significantly larger than during standing, and that the ratio of low- to high-frequency fluctuation during standing was significantly higher than during supine and sitting. This could be linked with the parasympathetic predominance during supine and sitting, and a shift to sympathetic dominance while standing. During spontaneous walking, the high-frequency fluctuation was significant lower than during supine. The low- to high-frequency ratio decreased significantly from standing to spontaneous walking, which may imply an increased vagal predominance (autonomic effect) or an increased respiratory activity (mechanical effect).