Domain knowledge

These feature sets will take use of the previous research done at The Intervention Centre, Oslo, summarized in section 2.2.3.

Feature set one - mid-systole Halvorsen et al. [30] studied the effects of esmolol, infusion and ischemia. When ischemia is present, it was stated that there were“significant decreases in accelerometer measurements of midsystolic velocity. (...) The greatest change in the accelerometer measurements, however, was found in midsystolic displacement, which demonstrated negative values”. This can be seen in figure 3.9. Regarding esmolol, Halvorsen et al. stated that“automated accelerometer measurements, midsystolic velocity (...) and midsystolic displacement all decreased, but reductions were smaller than seen with LAD occlusion (ischemia).”

Figure 3.9: Figure showing velocity and displacement in circumferential direction during baseline and ischemia. Thick lines indicate the middle of

systole, as approximated by Halvorsenet al[30].

.

Namely the middle of systole, mentioned in section 2.1.2, shows the best diversity between the heart functions (baseline, esmolol and ischemia). Ad-ditionally, the direction showing best diversity is the circumferential direc-tion.

Similar results were found by Grymyret al.[23] stating that “circumferen-tial peak mid-systole demonstrated high sensitivity and specificity for detecting regional myocardial ischemia.” This is also seen in figure 3.10, where also epinephrine (adrenaline), fluid (fluid loading) and esmolol are included.

Ephinedrine, fluid and esmolol are marked in the figure as global interven-tions, as they do not target a distinct region of the heart while ischemia is simulated through an occlusion of a specific blood vessel of the heart. All dysfunctions are described in section 2.2.2

Figure 3.10: Figure showing average peak midsystolic velocity, denoted V_sys. The bottom figure is from an accelerometer mounted in the basal

region of the heart (close to the top), but these measurements were discarded. The top figure is from an accelerometer mounted on the apical

region (close to the bottom).Vsysis shown during all four interventions (LAD occlusion is ischemia) in addition to baseline (healthy).±standard

deviation is also shown. [23].

Since the effects of ischemia and esmolol are more prominent in the middle of systole and in the circumferential direction, interesting features are:

• Peak acceleration within the first 150ms from R-peak

• Peak velocity within the first 150 ms from R-peak

3.3. FEATURE EXTRACTION

• Average acceleration of the first 150 ms from R-peak

• Average velocity of the first 150 ms from R-peak

• Difference in displacement from R-peak to 150ms after R-peak

• Minimum acceleration within the first 150ms from R-peak

The middle of systole is approximated by Halvorsenet al. [30] to start at R-peak (see 2.1.4), and last for 150 ms.

Notice in figure 3.10 that the peak midsystolic velocity in the circumfer-ential direction, from high to low goes: ephinedrine (adrenaline), Fluid (fluid loading), baseline, esmolol, and LAD occlusion (ischemia). The ve-locity in baseline, fluid and esmolol shows little to no difference between them, hence not all classes are expected to achieve a great accuracy using the aforementioned features.

Feature set two - IVR-phase This drawback can be alleviated by includ-ing the isovolumetric relaxation (IVR) phase, described in section 2.1.2. Ac-cording to Halvorsen et al. [28], increases in accelerometer velocities in the isovolumetric relaxation (IVR) phase were observed in all directions dur-ing ischemia. A significant change, however, was only found in the cir-cumferential direction. This can be seen in figure 3.11. It would therefore be interesting to look at six features listed from feature set one, but in the IVR phase:

• Peak acceleration in the IVR phase

• Peak velocity between in the IVR phase

• Average acceleration in the IVR phase

• Average of velocity in the IVR phase

• Difference in displacement from R-peak to middle of IVR phase

• Minimum acceleration in the IVR phase

The IVR phase is approximated to start 290 ms after R-peak and last 100 ms. The approximation of the IVR phase is somewhat vague, hence is a weakness with the features.

Looking at figure 3.10, the circumferential direction is not the only direction showing diversity among classes. Both longitudinal and radial directions shows some diversity between dysfunctions. It would therefore be interest-ing to investigate these directions in the same manner as in circumferential direction. That is, feature set one and two applied to the longitudinal and radial direction.

Another reason for inspecting all directions is that it is of interest to see which direction and phase (midsystolic og IVR phase) is most prominent.

Figure 3.11: Representative left ventricular curves from a single patient of accelerometer circumferential velocity and acceleration during baseline

and preconditioning LAD occlusion (ischemia) [28]

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That is, which direction and phase changes most when a dysfunction is present. It is already hypothesized that circumferential direction changes most, although it would be interesting to see if the model built, concluded likewise.

Feature set three - Frequency It would also be interesting to investigate features regarding the frequency of the cycles. In the adrenaline interven-tion, the heart rate will increase, while the opposite happens with esmolol, fluid loading and ischemia, though with varying degree. Consequently, one can expect there to be some frequency characteristic pattern in the classes. Consequently, the created features are:

• The 3 highest frequency components of the acceleration in circum-ferential direction. The adrenaline dysfunction is expected to have higher frequency components than the rest. These features were also used by Preeceet al.[50] when doing activity classification using an accelerometer. It was concluded that these features were the best fea-tures among four other time- and frequency domain feafea-tures and five other wavelet features.

• Elle et al. [19] proposed using a difference value based on the FFT frequency pattern because of the difference in the frequency domain between baseline and ischemia. It would therefore be interesting

3.4. TRAINING AND TESTING

to accumulate the magnitude of the frequency components in the circumferential direction.

• Number of samples used to represent a cycle.

The number of samples used to represent a cycle is highly depended on the dysfunction since the dysfunction influences the heart rate. The average number of samples used to represent a cycle were calculated and can be seen in table 3.3. The number of samples were scaled into the [0, 1] range since the sampling rate were either 250Hz and 500Hz.

Classes Average

Table 3.3: Table showing the average number of samples used for each cardiac dysfunction. The results were scaled into the range [0, 1].