Re Gregg

Philips medical systems support for open ECG and standardization efforts

Philips Medical Systems developed a new ECG data formar based on XML technology in response to increased demands fir ECG data access and better ECG device interoperability. Philips has published the schema for the XML-based ECG format that is used by its electrocardiographs, bedside monitors, and defibrillators. To assist access to ECG data, Philips provides users with U set of soJnvare tools for XML-ECG wuveforna manipulation and display. in addition, Philips Medical Systems has been strongly supporting all national and international initiatives that are underway to standardize ECG formats and improve interoperabiliiy between diagnostic ECG devices and healthcare information systems.

Limitations on the Re-use of patient specific coefficients for 12-lead ECG reconstruction

Patient specific coefficients for reconstructing missing precordial leads (patient-specific single-use or PSS) show good performance but require a 12-lead ECG to start monitoring. A more convenient approach is either the use of population based coefficients (POP) or patient specific coefficients from an old 12-lead ECG (patient-specific multi-use or PSM). We used a data set of 1493 resting 12-lead ECGs from 224 patients. Waveform comparisons were made between recorded 12-lead and reconstructed cases using RMS difference. Three cases were compared, PSS, PSM and POP. Median RMS reconstruction error in the ST-T region was 16, 46 and 40 muV for lead configuration V1/V4 in the PSS, PSM and POP cases respectively. For the V2/V5 configuration, median ST-T RMS error was 8, 40 and 41 muV. The RMS error for the PSS case was lower and significantly better by paired T-test. The difference between the two more convenient use-models, PSM and POP, was not significant. Population based coefficients are preferred over patient-specific coefficients if the single-use use-model cannot be followed.

A novel heart rate variability index for evaluation of left ventricular function using five-minute electrocardiogram

In this paper, we introduce a new index based on the frequency-domain analysis of heart rate variability, or more precisely, the power spectrum of the instant heart rate signal. This index, called VHFI, is defined as the very high frequency component of the power spectrum normalized to represent its relative value in proportion to the total power minus the very low frequency component. We tested VHFI on patients with known reduced left ventricular function and found that this index has the potential to be a useful tool for quick evaluation of left ventricular function.

Comparison of two automated methods for QT interval measurement

In this paper we compared two methods of automated QT interval measurement on standard ECG databases: the Root-Mean-Square (RMS) lead combining method aimed at QT monitoring and the method of median of lead-by-lead QT interval measurements. We used the PhysioNet PTB (N=548) and CSE measurement (N=125) standard databases. Both have reference QT interval measurements from a group of annotators. The last 10 seconds of each PTB record was downsampled from 1000 sample per second (sps) and an amplitude resolution of 1 muV to 500 sps and 5 muV in order to match the CSE set. PTB records #205 and #557 were excluded due to ventricular paced rhythm and artifact, respectively. Twenty five cases were excluded from the CSE set to match the selection of cases for IEC algorithm testing (IEC 60601-2-51). We processed all records using the Philips resting 12- lead ECG algorithm to generate representative beats for QT interval measurement. The RMS method measures QRS onset and end of Ton an RMS waveform constructed from 9 leads I, II, III and V1-V6. The lead-by-lead method takes the median QT interval across leads. The automated QT intervals by the RMS and lead-by-lead methods were compared to the reference manual QT measurements. The mean difference between the lead-by-lead QT and the reference QT was 1.7plusmn9.7 ms and 12.4plusmn23.0 ms (mean plusmnstandard deviation (SD)) for the CSE and PTB sets respectively. For the RMS method, the mean difference was -2.8plusmn11.1 ms and 10.3plusmn20.9 ms. F-tests indicate that the standard deviation between methods is not significantly different for the CSE set (P=0.18) or the PTB set (P=0.77). The lead-by-lead and RMS methods perform similarly, leading to the conclusion that the choice between them should be based on considerations such as the number of leads available or computational efficiency.

Right precordial leads V4R and V5R in ECG detection of acute ST elevation mi associated with proximal right coronary artery occlusion

ST elevation myocardial infarction (STEMI) in the right ventricle (RV) associated with right coronary artery (RCA) occlusion is known to have high hospital mortality. The hypothesis tested in this study is: right precordial leads V4R and V5R help detect STEMI in the right ventricle. ECGs from 1,970 subjects were collected in Ruijin Hospital (n=1,342), Shanghai, China and Lund University Hospital, Lund (n=565), Sweden. All ECGs were recorded with additional leads on the right precordial location in V4R and V5R. Our results show that the subjects with middle to upper RCA occlusion often show ST elevation in leads V4R and V5R and ST depression in lateral leads I, aVL, V5-V6, and are often undetected as STEMI or AMI in the standard 12-lead ECG. We conclude that adding V4R and V5R to standard ECG recording in assessing patients presenting with acute coronary syndrome is an easy and convenient way to increases the sensitivity of STEMI detection

Sources of Variability In Qt Calculations

is time consuming and therefore has not been applied in clinical practice. Automation of this score could facilitate clinical application. Therefore, we aimed to develop and validate an automatic algorithm for the AW-score. Methods: The AW-score (obtained from presenting ECG), assesses changes in ST-T-segments, T-waves and Q-waves. Each lead is designated an acuteness phase (1A, 1B, 2A or 2B) and the overall score is calculated. AW-score ranges from 1 (late ischemia/least acute) to 4 (early ischemia/most acute) and is calculated from the formula: AW-score = [(4 × (#leads 1A) + 3 × (#leads 1B) + 2 × (#leads 2A) + 1 × (#leads 2B))/(∑#leads with 1A, 1B, 2A or 2B)]. We developed an algorithm to automatically determine AW-score. The algorithm was designed using 50 ECGs. Each ECG lead (except aVR) was manually scored according to AW-score by two independent experts (Exp1 and Exp2) and automatically by our designed algorithm (auto-score). An adjudicated manual score (Adj-score) was determined between Exp1 and Exp2. The inter-rater reliabilities (IRRs) between Exp1 vs Exp2, and Adj-score vs auto-score were assessed by interclass correlation coefficient (ICC). Results: The Adj-score and auto-score had median AW-score 2.7 (1.75–3.52) and 3.0 (2.32–3.80), respectively. The IRR forAW-score betweenAdj-score and auto-score was ICC = 0.64 (CI 0.36–0.80), p b 0.001). Substantial differences in AW-score between Adj-score and auto-score were due to difference in measures of Q-wave duration. The IRR for AW-score between Exp1 and Exp2 was ICC = 0.89 (confidence interval (CI) 0.79–0.93, p b 0.0001). Conclusion: We have developed an automatic algorithm for measurement of AW-score. The preliminary test result was near acceptable for the inter-rater reliability between manual Adj-score and auto-score. More adjustments are needed to improve the measure of agreements between manual score and automatic algorithm for AW-score.