No abstract

The relative values of the unprocessed signal-averaged electrocardiogram (ECG) and time domain analysis and frequency domain analysis of the signal-averaged ECG were compared in 36 patients with sustained monomorphic ventricular tachycardia and a remote myocardial infarction, in 29 asymptomatic patients with a remote myocardial infarction and in 23 normal subjects. Area ratios of the energy spectra derived from fast Fourier transform analysis were calculated using six separate 140 ms intervals starting at 0, 40, 50 and 60 ms after QRS onset; 40 and 50 ms before QRS end and a variable length interval starting 40 ms before QRS end and extending to the T wave. Total filtered QRS duration, late potential duration and root mean square voltage of the terminal QRS complex were measured from the filtered vector magnitude signal-averaged ECG. The total QRS duration was also measured from the X, Y, Z leads of the unfiltered signal-averaged ECG. Seven variables were significantly different in univariate tests between myocardial infarction patients with and without ventricular tachycardia: three fast Fourier transform area ratios with the sampling interval starting at 1) QRS onset (p = 0.007), 2) 40 ms after QRS onset (p = 0.02), and 3) 60 ms after QRS onset (p less than 0.0001); and all four time domain variables at 1) total filtered QRS duration (p less than 0.0001), 2) late potential duration (p = 0.0001), 3) root mean square terminal QRS voltage (p = 0.0001), and 4) QRS duration from the unprocessed signal-averaged ECG (p less than 0.0001). Of these seven variables, only the fast Fourier transform area ratio starting at QRS onset was significantly different between patients with myocardial infarction without ventricular tachycardia and normal subjects. In multi-variable analysis, the total filtered vector magnitude QRS duration, a time domain variable that includes the late potential, was the only independent factor that separated patients with myocardial infarction with and without associated ventricular tachycardia.

Patients with increased intracranial pressure (ICP) caused by hydrocephalus or brain injury have poor brain compliance or increased brain stiffness. The condition is commonly treated by a surgical diversion of cerebrospinal fluid (CSF) through placement of a ventriculoperitoneal (VP) shunt. These inserted devices frequently fail and require replacement. Assessment of failed devices typically requires an invasive surgical procedure to implant an ICP sensor. Brain compliance can be determined non-invasively by comparing the intracranial pressure (ICP) waveform to the digital artery waveform. The ICP waveform is derived from a piezo sensor snugged into the external ear canal and worn as a headset. The digital artery waveform is derived from a stand pulse oximeter. Digital signal processing performed on sampled data from these two sensors shows a time-lag or phase relationship between the two waves which widens with worsening brain stiffness or compliance. An algorithm is presented that shows how these signals can be used to compute brain compliance. An instrument designed to calculate real-time brain compliance to aid healthcare professionals is described.

Non-invasive techniques to explore intracranial compliance and pressure have been extensively explored in recent years. Previous techniques have used expensive technologies to make these measurements, often with difficulty. We present a novel, inexpensive method and algorithm to observe trends in intracranial compliance measurement targeted towards the treatment and management of hydrocephalus. The technique uses two photo-plethysmographic sensors to record arterial pulse perfusion, a common tilt table apparatus to methodically and artificially increase intracranial pressure, and a digital signal processing algorithm to determine phase difference between the waveforms. A secondary phase-difference disease signature approach is also hypothesized.