Biomedical subjects

Philip de Chazal

Publications and source records attributed to Philip de Chazal.

8 recordsLinked to original sources

PubMed2006

Heart and respiration rate changes in the neonate during electroencephalographic seizure.

An investigation of changes in the neonatal electrocardiogram (ECG) and respiration signals from labelled seizure data for five neonatal patient records is reported. A decrease during seizure of 5.70% in the mean RR-interval was found. The mean respiration rate per epoch was found to decrease by a mean 18.44% during seizure. No significant change in the RR-interval standard deviation or in the mean respiration amplitude during seizure was observed. These results raise the possibility of using ECG and respiration based methods instead of existing electrcoenceplogram (EEG) based methods, or in concert with EEG-based neonatal seizure detection methods to improve on previously reported seizure detection methods.

Analysis of Variance↗

PubMed2006

Telephony-based voice pathology assessment using automated speech analysis.

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.

Algorithms↗

PubMed2005

A parametric feature extraction and classification strategy for brain-computer interfacing.

Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.

Adult↗

PubMed2004

Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.

STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.

Adult↗

PubMed2004

Non-episode-dependent assessment of paroxysmal atrial fibrillation through measurement of RR interval dynamics and atrial premature contractions.

A method is presented for classifying a single lead surface electrocardiogram recording from a Holter monitor as being from a subject with paroxysmal atrial fibrillation (PAF) or not. The technique is based on first assessing the likelihood of 30-min segments of electrocardiogram (ECG) being from a subject with PAF, and then combining these per-segment likelihoods to form a per-subject classification. The per-segment assessment is based on the output of a supervised linear discriminant classifier (LDC) which has been trained using known data from the Physionet Atrial Fibrillation Prediction Database (which consists of two hundred 30-min segments of Holter ECG, taken from 53 subjects with PAF, and 47 without). One of two LDCs is used depending on whether there is a significant correlation between observed low-frequency and high-frequency spectral power in the RR power spectral density over the 30-min segment. If there is high correlation, then the LDC uses spectral features calculated over a 10-min window; in the low-correlation case, both spectral features and atrial premature contractions are used as features. The classifier was tested for its ability to distinguish PAF and non-PAF segments using three independent data sets (representing a total of 1370 segments from 50 subjects). The cumulative sensitivity, specificity, and accuracy on a per-segment basis were 43.0, 99.3, and 80.5%, respectively on these independent test sets. By combining the results of segment classification, a per-subject classification into PAF and non-PAF classes was performed. For the 50 subjects in the independent data sets, the sensitivity and specificity of the per-subject classifier were 100%.

Algorithms↗

PubMed2004

Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram.

An automated classification algorithm is presented which processes short-duration epochs of surface electrocardiogram data derived from polysomnography studies, and determines whether an epoch is from a period of sleep disordered respiration (SDR) or normal respiration (NR). The epoch lengths considered were 15, 30, 45, 60, 75, and 90 s. Epochs were labeled as 'NR' or 'SDR' by a human expert, based on standard polysomnography interpretation rules. The automated classification algorithm was trained and tested on a database of 70 overnight ECG recordings from subjects with and without obstructive sleep apnoea syndrome (35 used for training, 35 for independent validation). Depending on the epoch length, the classifier correctly labeled between 87% (15 s epochs) and 91% (60 s epochs) of the epochs in the test set. Accuracy was lowest for the shortest (15 s) and longest (90 s) epoch lengths, but the analysis was relatively insensitive to choice of epoch length. The classifications from these epochs were combined to form an overall summary measure of minutes-of-SDR, allowing per-subject classification.

Adult↗

PubMed2004

Automatic classification of heartbeats using ECG morphology and heartbeat interval features.

A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50,000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

Algorithms↗

PubMed2003

Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea.

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.

Adult↗