A portable prototype magnetometer to differentiate ischemic and non-ischemic heart disease in patients with chest pain

Background: Magnetocardiography (MCG) is a non-invasive technique used to measure and map cardiac magnetic fields. We describe the predictive performance of a portable prototype magnetometer designed for use in acute and routine clinical settings. We assessed the predictive ability of the measurements derived from the magnetometer for the ruling-out of healthy subjects and patients whose chest pain has a non-ischemic origin from those with ischemic heart disease (IHD). Methods: MCG data were analyzed from a technical performance study, a pilot clinical study, and a young healthy reference group. Participants were grouped to enable differentiation of those with IHD versus non-IHD versus controls: Group A (70 IHD patients); Group B (69 controls); Group C (37 young healthy volunteers). Scans were recorded in an unshielded room. Between-group differences were explored using analysis of variance. The ability of 10 candidate MCG predictors to predict normal/abnormal cases was analyzed using logistic regression. Predictive performance was internally validated using repeated five-fold cross-validation. Results: Three MCG predictors showed a significant difference between patients and age-matched controls (P<0.001); eight predictors showed a significant difference between patients and young healthy volunteers (P<0.001). Logistic regression comparing patients with controls yielded a specificity of 35.0%, sensitivity of 95.4%, and negative predictive value for the ruling-out of IHD of 97.8% (area under the curve 0.78). Conclusion: This analysis represents a preliminary indication that the portable magnetometer can help rule-out healthy subjects and patients whose chest pain has a non-ischemic origin from those with IHD

Development of a Low Cost Bio-magnetometer for the Detection of Myocardial Infarction

The aim of the present study was to design, develop and examine a portable and low cost magnetometer for cardiac imaging. Magnetocardiography (MCG) involves capturing magnetic field maps (MFM's) of current distributions resulting from cardiac action potentials. Particular emphasis was given to the diagnostic performance of distinguishing cardiac patients, mainly with ischemic heart disease, from healthy individuals with the aim of potentially expanding the clinical utility. The current magnetocardiography is known as a superconducting quantum interference device (SQUID). It was first used in 1970 in a magnetically shielded room and the device produced an MCG waveform that could be visualised just as clearly as the conventional electrocardiogram. This popularity is largely attributable to the appearance of unshielded SQUID devices in the late 1990s. However, continued reliance on large immovable equipment that is expensive to maintain, has limited the clinical application of MCG to a small but expanding group of research clinicians worldwide. Despite this, a large body of evidence suggests that an unshielded MCG device is a potentially powerful tool for detecting a multitude of cardiac abnormalities in the human heart. Over the course of this research, I have constructed an induction coil magnetometer that is not only portable, low cost and non-invasive, but also reaches into the sensitivity range needed for magnetocardiography and produces magnetic field maps that are medically diagnosable. Therefore, this technology provides a real alternative to other costly diagnostic tools, such as Superconducting Quantum Interference device-SQUIDs. While MCG sensors are an established diagnostic tool to diagnose cardiac conditions, for other groups around the world [1], this device is a newly developed apparatus using a less complicated design. This study determines the extent to which the device can provide a clear image across test subjects with a range of age and genders and to identify which parameters from the MCG data are markers for ischemic heart disease when compared with healthy controls. A pilot study was conducted using 40 patients with ischemia and 20 with post myocardial infarction (MI) as well as 60 healthy age matched controls to evaluate the capability of MCG to detect myocardial ischemia. To identify the optimal recording of MCG, we looked at 55 patients with ischaemic heart disease and 10 post MI patients. In addition, 49 healthy age matched controls underwent examinations. In the MCG pilot study, we defined standard features or \lobes" of MCG signals, which are common to all healthy normal subjects. For the purpose of evaluating the diagnostic performance of the MCG these are referred to as cursors, and consist of 6 parameters that can be estimated, QR and RS, T-wave (T1-T4), as well as the RT interval and zero crossings (ZCs). We also consider QR length and RS length, T length, and angles and genders. They were examined in the detection of myocardial ischemia in both the patient group and the healthy controls. The results showed that MCG is able to distinguish patients from healthy regardless of the presence or absence of a history of remote MI. Stepwise regression was performed using the Forward Logistic Regression-LR method. Using this approach, any variable with p > 0:20 was removed at each step. Thereafter, the two-way interactions were included in a stepwise analysis with other significant predictors. Candidate predictors entered at step 1 include the following; T2 corr by T2 length, QR cursor, QR angle, T1 angle, T zcMDIS, QR length by RS length, and GENDER(1) by T stdcorrvar. The aforementioned parameters are spatial and temporal diagnostic variables of the MCG that we used in o ur study to distinguish healthy from patient. T1 is the magnetic Field Map (MFM) at the beginning of depolarization and T2 is the MFM at the peak of the depolarsation. Length, angle and correlation of each of these were measured. We also defined some other parameters such QR that is repolarisation at the peak of Rwave. More definitions are detailed in chapter 6. As a result of the final model, we find the following interactions are significant predictors: QR length by RS length, GENDER(1) by T stdcorrvar and T2 corr by T2 length. In conclusion, 15 channel- MCG can detect myocardial ischemia at rest and supine position in an unshielded environment. In MCG several factors can be used to distinguish unhealthy from healthy: QR, RS length and T-wave correlations. The interactions between some of the parameters can also produce significant predictions. We also found that T-wave changes during the period of time corresponding to T1 to T4 are very useful in detecting abnormalities. Furthermore, the T-wave parameters such as angles and lengths may contain information on separate physiological stages of the ischemia development. This will be subject to future research

CONSORT Diagram: Pilot clinical study.

<p>Participant flow through the pilot clinical study. Data were analyzed for 15/21 patients and 18/21 healthy controls.</p

Apparent and cross-validation performance of the logistic regression models.

<p>Apparent and cross-validation performance of the logistic regression models.</p

Average value for each predictor for patient and control groups.

<p>Average value for each predictor for patient and control groups.</p

CONSORT Diagram: Technical performance study.

<p>Participant flow through the technical performance study. Data were analyzed for 55/63 patients and 51/60 healthy controls.</p

Confusion matrix for a binary classifier.

<p>Confusion matrix for a binary classifier.</p

Histogram of the RS_peak predictors.

<p>A representative histogram of the RS_peak predictors for study participants enrolled in Group A, Group B, and Group C.</p