Spray-on technique simplifies fabrication of complex thermal insulation blanket

Spray-on process constructs molds used in forming sections of thermal insulation blankets. The process simplifies the fabrication of blankets by eliminating much of the equipment formerly required and decreasing the time involved

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%)

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

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°]

Influence of the training set composition on the estimation performance of linear ECG-lead transformations.

Linear ECG-lead transformations (LELTs) are used to estimate unrecorded target leads by applying a number of recorded basis leads to a LELT matrix. Such LELT matrices are commonly developed using training datasets that are composed of ECGs that belong to different diagnostic classes (DCs). The aim of our research was to assess the influence of the training set composition on the estimation performance of LELTs that estimate target leads V1, V3, V4 and V6 from basis leads I, II, V2 and V5 of the 12-lead ECG. Our assessment was performed using ECGs from the three DCs left ventricular hypertrophy, right bundle branch block and normal (ECGs without abnormalities). Training sets with different DC compositions were used for the development of LELT matrices. These matrices were used to estimate the target leads of different test sets. The estimation performance of the developed matrices was quantified using root mean square error values calculated between derived and recorded target leads. Our findings indicate that unbalanced training sets can lead to LELTs that show large estimation performance variability across different DCs. Balanced training sets were found to produce LELTs that performed well across multiple DCs. We recommend balanced training sets for the development of LELTs

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible