LSTM for imbalanced time series classification

I wanted to fit simple LSTM model to perform binary classification on multivariate time series data. Since my data is severely imbalanced, I have integrated class_weight argument from sklearn in my model. However, I have got pretty high loss value, and it was not decreasing with each epoch. My f1 score was 0.018 which is extremely low as well. I appreciate your suggestions!

Sample data:

sequence_length = 10
def generate_data(X, y, sequence_length = 10, step = 1):
    X_local = []
    y_local = []
    for start in range(0, len(data) - sequence_length, step):
        end = start + sequence_length
        X_local.append(X[start:end])
        y_local.append(y[end-1])
    return np.array(X_local), np.array(y_local)

X_sequence, y = generate_data(data.loc[:, "V1":"V4"].values, data.Class)
model = keras.Sequential()
model.add(LSTM(100, input_shape = (10, 4)))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss="binary_crossentropy"
              , metrics=[keras.metrics.binary_accuracy]
              , optimizer="adam")

model.summary()

training_size = int(len(X_sequence) * 0.7)
X_train, y_train = X_sequence[:training_size], y[:training_size]
X_test, y_test = X_sequence[training_size:], y[training_size:]
from sklearn.utils import class_weight
class_weights = dict(zip(np.unique(y_train), class_weight.compute_class_weight('balanced', np.unique(y_train), 
                y_train)))
model.fit(X_train, y_train, batch_size=64, epochs=50,class_weight=class_weights)
model.evaluate(X_test, y_test)
y_test_prob = model.predict(X_test, verbose=1)
y_test_pred = np.where(y_test_prob > 0.5, 1, 0)
from sklearn.metrics import f1_score
f1_score(y_test, y_test_pred)