A Low-Cost Tonometer Alternative: A Comparison Between Photoplethysmogram and Finger Ballistocardiogram and Validation Against Tonometric Waveform

Hypertension is a silent killer and one-third of its sufferers are unaware of its presence. Tonometric devices, like SphygmoCor, Compilor etc., represent the gold standard in pulse wave velocity (PWV) and augmentation index (AIx) measurements which are limited by their high cost and operational accuracy. Here, we present an alternative technology that is low cost and may be suitable for the 'wearable' setting. We undertook the comparisons of arterial waveforms obtained by photoplethysmogram (PPG) and finger ballistocardiogram (BPP) sensors which were then validated against a SphygmoCor tonometric device. Specifically, the agreement analysis of the augmentation, stiffness, reflection, elasticity, ejection elasticity and dicrotic reflection indexes showed that arterial distension waveform sensing using BPP sensor, has precision and accuracy similar to that of a SphygmoCor tonometric device whilst outperforming the volumetric arterial flow sensing using a PPG sensor, in every index. BPP indexes showed the r 2 fit of up to 0.95 and Spearman's rank correlation up to 0.91 when validated against the SphygmoCor tonometer. The estimated individual transfer functions for the BPP sensor, with reference to SphygmoCor, have accuracies of above 85% and 98% for 2 and 4-element windkessel (WK) models, respectively. The findings reported in this work may also be useful for the development of systems that are beneficial in the early and/or routine detection of hypertension

The effects of 40 Hz low-pass filtering on the spatial QRS-T angle

The spatial QRS-T angle (SA) is a vectorcardiographic (VCG) parameter that has been identified as a marker for changes in the ventricular depolarization and repolarization sequence. The SA is defined as the angle subtended by the mean QRS-vector and the mean T- vector of the VCG. The SA is typically obtained from VCG data that is derived from the resting 12-lead electrocardiogram (ECG). Resting 12-lead ECG data is commonly recorded using a low-pass filter with a cutoff frequency of 150 Hz. The ability of the SA to quantify changes in the ventricular depolarization and repolarization sequence make the SA potentially attractive in a number of different 12-lead ECG monitoring applications. However, the 12-lead ECG data that is obtained in such monitoring applications is typically recorded using a low-pass filter cutoff frequency of 40 Hz. The aim of this research was to quantify the differences between the SA computed using 40 Hz low- pass filtered ECG data (SA40) and the SA computed using 150 Hz low-pass filtered ECG data (SA150). We assessed the difference between the SA40 and the SA150 using a study population of 726 subjects. The differences between the SA40 and the SA150 were quantified as systematic error (mean difference) and random error (span of Bland-Altman 95% limits of agreement). The systematic error between the SA40 and the SA150 was found to be -0.126° [95% confidence interval: -0.146° to - 0.107°]. The random error was quantified 1.045° [95% confidence interval: 0.917° to 1.189°]. The findings of this research suggest that it is possible to accurately determine the value of the SA when using 40 Hz low-pass filtered ECG data. This finding indicates that it is possible to record the SA in applications that require the utilization of 40 Hz low-pass ECG monitoring filters

The effects of electrode placement on an automated algorithm for the detection of ST segment changes on the 12-lead ECG

In this study we investigate the effect that ECG electrode placement can have on the detection of ST segment changes. BSPMs from 45 subjects undergoing PTCA were analysed (15 left anterior descending, 15 left circumflex and 15 right coronary artery). 12-lead ECG were extracted from BSPMs corresponding with correct precordial electrode positioning and corresponding with simultaneous vertical movement of all of the precordial leads in 5mm increments up to +/-50mm away from the correct position. A computer algorithm was developed based on current guidelines for the detection of STEMI and Non-STEMI. This algorithm was applied to all of the extracted 12-lead ECGs. Median sensitivity and specificity, based upon all baseline versus all peak balloon inflation cases, were calculated for results generated at each electrode position. With the precordial leads positioned correctly the sensitivity and specificity were 51.1% and 91.1% respectively. When all precordial leads were placed 50mm superior to their correct position the sensitivity increased to 57.8% whilst specificity remained unchanged. At 50mm inferior to the correct position the sensitivity and specificity were 46.7% and 88.9% respectively. The results show a variation of more than 10% in sensitivity when the electrodes are moved up to 100mm vertically

Automated detection of atrial fibrillation using RR intervals and multivariate-based classification

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%)

On the derivation of the spatial QRS-T angle from Mason-Likar leads I, II, V2 and V5

The spatial QRS-T angle (SA) has been identified as a marker for changes in the ventricular depolarization and repolarization sequence. The determination of the SA requires vectorcardiographic (VCG) data. However, VCG data is seldom recorded in monitoring applications. This is mainly due to the fact that the number and location of the electrodes required for recording the Frank VCG complicate the recording of VCG data in monitoring applications. Alternatively, reduced lead systems (RLS) allow for the derivation of the Frank VCG from a reduced number of electrocardiographic (ECG) leads. Derived Frank VCGs provide a practical means for the determination of the SA in monitoring applications. One widely studied RLS that is used in clinical practice is based upon Mason-Likar leads I, II, V2 and V5 (MLRL). The aim of this research was two-fold. First, to develop a linear ECG lead transformation matrix that allows for the derivation of the Frank VCG from the MLRL system. Second, to assess the accuracy of the MLRL derived SA (MSA). We used ECG data recorded from 545 subjects for the development of the linear ECG lead transformation matrix. The accuracy of the MSA was assessed by analyzing the differences between the MSA and the SA using the ECG data of 181 subjects. The differences between the MSA and the SA were quantified as systematic error (mean difference) and random error (span of Bland-Altman 95% limits of agreement). The systematic error between the MSA and the SA was found to be 9.38° [95% confidence interval: 7.03° to 11.74°]. The random error was quantified as 62.97° [95% confidence interval: 56.55° to 70.95°]

Morphology-based detection of premature ventricular contractions

Premature ventricular contraction (PVC) is the type of ectopic heartbeat, commonly found in the healthy population and is often considered benign. However, they are reported to adversely affect the accuracy of R-R variability based electrocardiographic (ECG) algorithms. This study proposes a Principal Component Analysis (PCA) based algorithmic approach to detect the PVCs based on their morphology. The eigenvectors were derived from signal window around the R-peak, where signal window for the PVC (wPVC) and that of NSR (wNSR) were set to 0.55 seconds and 0.16 seconds respectively. We used 24 ECG recordings from MIT BIH arrhythmia database as training dataset and the remaining 24 ECG recordings as testing dataset. Using the derived eigenvectors and the Linear regression (LR) analysis; complexes corresponding to the wNSR and wPVC were estimated from training and testing datasets. Four different classification methods were employed to differentiate between wPVS and wNSR, namely, Root mean squared error (RMSE), Pearson product-moment correlation coefficient comparision, Histogram probability distribution and k-Nearest Neighbour (KNN). All four methods were implemented individually to classify the wPVC and wNSR. The performance of each of the classification approach was evaluated by computing sensitivity and specificity. With the sensitivity of 93.45% and specificity of 93.14%, KNN based classification method has shown the best performance. The method proposed in this study allows for an effective differentiation between NSR beats and PVC beats