Christina Orphanidou

Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.

Testing of Wearable Monitors in a Real-World Hospital Environment: What Lessons Can Be Learnt?

If wearable sensors are to play a significant role in monitoring the vital signs of hospitalised patients they need to be accepted by doctors and other healthcare workers. To gain this acceptance, evidence of their effectiveness needs to be demonstrated in clinical trials. In this pragmatic feasibility study four commercially-available, CE-marked sensors were combined into three monitoring systems and used to record the electrocardiograms (ECGs) and photoplethysmograms (PPGs) of 31 hospitalised patients, to determine whether the sensors could collect vital sign data reliably enough for use in larger clinical trials. Patients were asked to wear the sensors for 24 hours. Out of the 31 studies, on only 3 occasions did any of the monitoring systems manage to record both ECG and PPG data for the full 24-hour duration. The causes for the failure of sensors to record data from in-hospital patients consistently are discussed and a clinical perspective is given on the design features needed for a sensor to be usable in a hospital setting.

What marks the beat of speech

Which acoustic properties of the speech signal differ between rhythmically prominent syllables and non-prominent ones? A production experiment was conducted to identify these acoustic properties. Subjects read out repetitive text to a metronome, trying to match stressed syllables to its beat. The analysis searched for the function of the speech signal that best predicts the timing of the metronome ticks. The most important factor in this function is found to be the contrast in loudness between a syllable and its neighbors. The prominence of a syllable can be deduced from the specific loudness in an (approximately) 360 ms window centered on the syllable in question relative to an (approximately) 800-ms-wide symmetric window.

Telemetry-based vital sign monitoring for ambulatory hospital patients

Early detection of deterioration in hospital patients followed by intervention and stabilization can prevent adverse events such as a cardiac arrest, unscheduled admission to ICU, or death. Patients at step-down units of hospitals tend to have their vital signs checked by nursing staff at 4-hourly intervals. If an abnormality develops in the period between nurse observations, it is likely to lead to an adverse event (which may have been preventable). Visensia is a real-time, continuous vital sign acquisition system, using data fusion in order to predict patient deterioration. Validation trials have shown that the system successfully provides early warning of adverse events, such as cardiac arrests. We tested the system on lower acuity, ambulatory patients in a hospital ward with the vital signs being collected using telemetry. In order to optimize processing, we have developed an algorithm for deriving the respiration rate of the patient from the ECG signal.

Spectral fusion for estimating respiratory rate from the ECG

A new method for extracting respiratory signals from the electrocardiogram (ECG) is proposed. The method performs AR spectral analysis on heart rate variability and beat morphology information extracted from the ECG and identifies the closest matched frequencies which then provide an estimate of the respiration frequency. Fusing frequency information from different sources reliably rejects noise and movement-induced artefact and is promising for application to ambulatory hospital data. The performance of the method was validated on two databases of simultaneously recorded ECG and reference respiration signals. The spectral fusion technique is found to correctly estimate respiratory rate 90% of the time in the case of non-ambulatory data and 86% of the time in the case of ambulatory data with a root mean square error of 0.92 and 1.40 breaths per minute, respectively.

Image quality in non-gated versus gated reconstruction of tongue motion using magnetic resonance imaging: a comparison using automated image processing

PurposeThe use of gated or ECG triggered MR is a well-established technique and developments in coil technology have enabled this approach to be applied to areas other than the heart. However, the image quality of gated (ECG or cine) versus non-gated or real-time has not been extensively evaluated in the mouth. We evaluate two image sequences by developing an automatic image processing technique which compares how well the image represents known anatomy.MethodsFour subjects practised experimental poly-syllabic sentences prior to MR scanning. Using a 1.5 T MR unit, we acquired comparable gated (using an artificial trigger) and non-gated sagittal images during speech. We then used an image processing algorithm to model the image grey along lines that cross the airway. Each line involved an eight parameter non-linear equation to model of proton densities, edges, and dimensions.ResultsGated and non-gated images show similar spatial resolution, with non-gated images being slightly sharper (10% better resolution, less than 1 pixel). However, the gated sequences generated images of substantially lower inherent noise, and substantially better discrimination between air and tissue. Additionally, the gated sequences demonstrate a very much greater temporal resolution.ConclusionOverall, image quality is better with gated imaging techniques, especially given their superior temporal resolution. Gated techniques are limited by the repeatability of the motions involved, and we have shown that speech to a metronome can be sufficiently repeatable to allow high-quality gated magnetic resonance imaging images. We suggest that gated sequences may be useful for evaluating other types of repetitive movement involving the joints and limb motions.

A method for assessing the reliability of heart rates obtained from ambulatory ECG

In this paper we present a method of assessing the reliability of heart rates (HRs) obtained from ambulatory ECGs. Our method assigns a Reliability Index (RI) to ECG segments based on a set of physiologically relevant rules prior to using a template matching approach. We validated the algorithm on 1500 manually annotated samples of ECG taken from two different studies and using three different sensors at different sampling rates. The sensitivity of our method was 98% and the specificity was 94%. Our method matched or was more conservative than the human annotations in 99.4% of the samples, making it a promising tool for inclusion in next-generation wearable sensors.

Multiscale Voice Morphing Using Radial Basis Function Analysis

A new multiscale voice morphing algorithm using radial basis function (RBF) analysis is presented in this paper. The approach copes well with small training sets of high dimension, which is a problem often encountered in voice morphing. The aim of this algorithm is to transform one person’s speech pattern so that it is perceived as if it was spoken by another speaker. The voice morphing system we propose assumes parallel training data from source and target speakers and uses the theory of wavelets in order to extract speaker feature information. The spectral conversion is modelled using RBF analysis. Independent listener tests demonstrate effective transformation of the perceived speaker identity.

2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)

In this paper we present a method of assessing the reliability of heart rates (HRs) obtained from ambulatory ECGs. Our method assigns a Reliability Index (RI) to ECG segments based on a set of physiologically relevant rules prior to using a template matching approach. We validated the algorithm on 1500 manually annotated samples of ECG taken from two different studies and using three different sensors at different sampling rates. The sensitivity of our method was 98% and the specificity was 94%. Our method matched or was more conservative than the human annotations in 99.4% of the samples, making it a promising tool for inclusion in next-generation wearable sensors.

Detecting rhythmical prominence in speech by an optimized convolution kernel

We present an approach for detecting rhythmical prominence in read speech. A production experiment was conducted during which subjects repetitively read out speech to a metronome, trying to match stressed syllables to its beat. In the analysis, we compute a function from the speech waveform, related to acoustic properties of speech such as specific loudness, pitch, voicing, and spectral slope. The function is then convolved with a Mexican Hat convolution kernel. Taking large maxima in the function to be predictions of the metronome ticks, we adjust the parameters of the signal to maximize the accuracy of the predictions. The parameters are adjusted by minimizing the phase variation between metronome ticks and ticks predicted from the audio, over a specified time interval. We confirm the results by Bootstrap resampling. We find that the most important factor is the contrast in specific loudness between a syllable and its neighbors. The prominence can be deduced from the specific loudness in an (approximately) 360 ms window centered on the syllable in question relative to an (approximately) 800 ms‐wide symmetric window.