Thuy T. Pham

Pulsed Doppler signal processing for use in mice: applications

We have developed a high-frequency, high-resolution Doppler spectrum analyzer (DSPW) and compared its performance against an adapted clinical Medasonics spectrum analyzer (MSA) and a zero-crossing interval histogram (ZCIH) used previously by us to evaluate cardiovascular physiology in mice. The aortic velocity (means /spl plusmn/ SE: 92.7 /spl plusmn/ 2.5 versus 82.2 /spl plusmn/ 1.8 cm/s) and aortic acceleration (8194 /spl plusmn/ 319 versus 5178 /spl plusmn/ 191 cm/s/sup 2/) determined by the DSPW were significantly higher compared to those by the MSA. Aortic ejection time was shorter (48.3/spl plusmn/ 0.9 versus 64.6 /spl plusmn/ 1.8 ms) and the isovolumic relaxation was longer (17.6 /spl plusmn/ 0.6 versus 13.5 /spl plusmn/0.6 ms) when determined by the DSPW because it generates shorter temporal widths in the velocity spectra when compared to the MSA. These data indicate that the performance of the DSPW in evaluating cardiovascular physiology was better than that of the MSA. There were no significant differences between the aortic pulse wave velocity determined by using the ZCIH (391 /spl plusmn/ 16 cm/s) and the DSPW (394 /spl plusmn/ 20 cm/s). Besides monitoring cardiac function, we have used the DSPW for studying peripheral vascular physiology in normal, transgenic, and surgical models of mice. Several applications such as the detection of high stenotic jet velocities (>4 m/s), vortex shedding frequencies (250 Hz), and subtle changes in wave shapes in peripheral vessels which could not obtained with clinical Doppler systems are now made possible with the DSPW.

A visual motion detecting module for dragonfly-controlled robots.

When imitating biological sensors, we have not completely understood the early processing of the input to reproduce artificially. Building hybrid systems with both artificial and real biological components is a promising solution. For example, when a dragonfly is used as a living sensor, the early processing of visual information is performed fully in the brain of the dragonfly. The only significant remaining tasks are recording and processing neural signals in software and/or hardware. Based on existing works which focused on recording neural signals, this paper proposes a software application of neural information processing to design a visual processing module for dragonfly hybrid bio-robots. After a neural signal is recorded in real-time, the action potentials can be detected and matched with predefined templates to detect when and which descending neurons fire. The output of the proposed system will be used to control other parts of the robot platform.

Unsupervised discrimination of motor unit action potentials using spectrograms

Single motor unit activity study is a major research interest because changes of MUAP morphology, MU activation, and MU recruitment provide the most informative part in diagnosis and treatment of neuromuscular disorders. Intramuscular recordings often provide a more than one motor unit activities, thus MUAP discrimination is a crucial task to study single unit activities. Most neurology laboratories worldwide still need specialists who spend hours to classify MUAPs. In this study, we present a new real-time unsupervised method for MUAP discrimination. After automatically detect MUAPs, we extract features of spectrogram images from the wavelet coefficients of MUAPs. Unlike benchmark methods, we do not calculate Euclidean distances which assumes a spherical distribution of data. Instead, we measure correlation between spectrogram images. Then MUAPs are automatically discriminated without any prior knowledge of the number of clusters as in previous works. MUAP were detected on a real data set with a precision PPV of 94% (tolerance of 2 ms). We obtained a similar result in MUAP classification to the reference. The difference in percentages of MU proportions between our method and the reference were 3% for MU1, 0.4% for MU2, and 12% for MU3. In contrast, F1-score for MU3 reached the highest level at 91% (PPV at the highest of 96.64% as well).

Automated quality control of forced oscillation measurements: respiratory artifact detection with advanced feature extraction

The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies for quality control and reducing artifacts in FOT measurements have yet to be standardized or validated. Many quality control procedures rely on either simple statistical filters or subjective evaluation by a human operator. In this study, we propose an automated artifact removal approach based on the resistance against flow profile, applied to complete breaths. We report results obtained from data recorded from children and adults, with and without asthma. Our proposed method has 76% agreement with a human operator for the adult data set and 79% for the pediatric data set. Furthermore, we assessed the variability of respiratory resistance measured by FOT using within-session variation (wCV) and between-session variation (bCV). In the asthmatic adults test data set, our method was again similar to that of the manual operator for wCV (6.5 vs. 6.9%) and significantly improved bCV (8.2 vs. 8.9%). Our combined automated breath removal approach based on advanced feature extraction offers better or equivalent quality control of FOT measurements compared with an expert operator and computationally more intensive methods in terms of accuracy and reducing intrasubject variability.NEW & NOTEWORTHY The forced oscillation technique (FOT) is gaining wider acceptance for clinical testing; however, strategies for quality control are still highly variable and require a high level of subjectivity. We propose an automated, complete breath approach for removal of respiratory artifacts from FOT measurements, using feature extraction and an interquartile range filter. Our approach offers better or equivalent performance compared with an expert operator, in terms of accuracy and reducing intrasubject variability.

Spike Sorting: Application to Motor Unit Action Potential Discrimination

While in Chaps. 4 and 5 two-class discrimination applications are demonstrated that the proposed feature engineering play an important part in improving the accuracy performance results, this chapter illustrates the contribution of the feature learning scheme in a classification problem where some class information (e.g., number of classes) is not predefined. For example, in application of motor unit action potential (MUAP) sorting for intramuscular electromyography (nEMG) data, called nEMG spike sorting.

Collective Anomaly Detection: Application to Respiratory Artefact Removals

In this chapter, data sets and feature learning result observations in respiratory artefact removal for lung function tests, specifically the forced oscillation technique (FOT) are presented. The first section introduces the FOT method and respiratory artefacts. The next two sections describe our FOT data sets and performance metrics used to evaluate the proposed scheme in Sect. 5. Section 6 discusses feature selection for FOT data, and two different models for artefact detectors are presented in Sects. 7 and 8. The last four sections reports results/discussion of feature ranking and performance comparison between our proposed detectors and existing methods.

A profiling analysis of contributions of cigarette smoking, dietary calcium intakes, and physical activity to fragility fracture in the elderly

Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A “lifestyle risk score” (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD.

A low-complexity algorithm for detection of atrial fibrillation using an ECG

OBJECTIVES We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. APPROACH Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. MAIN RESULTS On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. SIGNIFICANCE The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

Low-trauma rib fracture in the elderly: Risk factors and mortality consequence

PURPOSE Low trauma rib fracture (hereinafter, rib fracture) is common in the elderly, but its risk factors and mortality consequence are rarely studied. We sought to define the epidemiology of rib fracture and the association between rib fracture and postfracture mortality. METHODS The study was part of the Dubbo Osteoporosis Epidemiology Study, which was designed as a population-based prospective study, and consisted of 2041 women and men (aged ≥ 60). The incidence of rib fracture was ascertained from X-ray reports. Bone mineral density (BMD) was measured by DXA (GE-Lunar). The time-dependent Cox model was used to access the relationship between rib fracture and mortality. RESULTS During the median follow-up of 13 years, 59 men and 78 women had sustained a rib fracture, making the annual incidence of 4.8/1000 person-years. Each SD (0.15 g/cm2) lower in femoral neck BMD was associated with ~2-fold increase in the hazard of fracture (hazard ratio [HR] 1.9; 95% CI, 1.4 to 2.6 in men; and HR 2.1; 95% CI, 1.6 to 2.8 in women). Among those with a rib fracture, the incidence of subsequent fractures was 10.2/100 person-years. Compared with those without a fracture, the risk of mortality among those with a fracture was increased by ~7.8-fold (95% CI, 2.7 to 22.5) in men and 4.9-fold (95% CI 2.0 to 11.8) in women within the first year postfracture. CONCLUSIONS A rib fracture signifies an increased risk of subsequent fractures and mortality. The increased risk of mortality during the first 2.5 years postfracture suggests a window of opportunity for treatment.

Wearable healthcare systems: A single channel accelerometer based anomaly detector for studies of gait freezing in Parkinson's disease

The causality of gait freezing in patients with advanced Parkinsons disease is still not fully understood. Clinicians are interested in investigating the freezing of gait (FoG) histogram of patients in their daily life. To that end, one needs a real-time signal processing platform that can help record freezing information (e.g., timing and the duration of every gait freezing occurrences). Wearable wireless sensors have been proposed to monitor FoG epochs. Existing automated methods using accelerometers have been introduced with high accuracy performance only for subject-dependent settings (e.g., an individual offline training process). This is a troublesome for large scale out-of-lab deployment and time-consuming. In this work, we used spectral coherence analysis for accelerometer data to apply an anomaly detection approach. Conventional features such as energy and freezing index are introduced to help refine normal epochs while the anomaly scores from spectral coherence measures define FoG epochs. Using this new set of features, our new FoG detector for subject-independent settings achieves the mean ±SD sensitivity (specificity) of 89.2±0.3% (95.6 ± 0.3%). To our best knowledge, this is the best performance for automated subject-independent approaches in literature of freezing of gait detection.